# Analysis on the Establishment and Management of Library Resource Base Based on Modern Information Technology

Authors: Chiyue Yao; Vinh Phuc Dung
Journal: Wireless Communications and Mobile Computing
 (2022)

---

## Abstract

The current rapid development of modern network information technology, along with the arrival of this trend, has profoundly changed people’s living and learning methods. In the past, the management of book resources in the traditional period has gradually exposed many drawbacks, and it is difficult to meet the technological development of the information age. Combined with the complexity of book management parameters, high variable dimensions, and the characteristics of multiattribute data points including not only numerical attributes but also category attributes and mixed attributes, the fuzzy clustering algorithm is combined with attribute weighted optimization, and then, the optimization is derived. Iterative formula and form show a weighted clustering algorithm to perform cluster analysis on relevant influencing factors in book data management. Digital library is not a library entity: it corresponds to various real social activities of public information management and dissemination and is manifested in various new information resource organizations and information dissemination services. As a fuzzy association rule mining algorithm, FP-growth algorithm is obviously better than Apriori algorithm in execution efficiency. However, due to the lack of fuzzy attributes and the large space complexity, the FP-growth algorithm cannot achieve effective multilayer association rule mining when dealing with large transaction databases, such as library databases. First, the algorithm divides the large-scale book transaction database into several sub-databases according to the transaction of the first item and constructs the corresponding sub-FP-tree structure; then, the parent items that are not frequent items in the hierarchical tree are filtered out in real time to reduce the scanning space.


---

## Body

## 1. Introduction

Improving user service content is the infrastructure of the National Library of China [1]. One of the important tasks of digital library is also an important topic of digital library work [2]. Digital library is a library that processes and stores various illustrated documents with digital technology. In essence, it is a distributed information system made of multimedia. It stores various information resources with different carriers and geographical locations with digital technology, so as to facilitate cross regional, object-oriented network query, and dissemination. It involves the whole process of information resource processing, storage, retrieval, transmission, and utilization. After several years of construction and development, the construction of libraries in my country, especially the composite library form represented by digital libraries and supplemented by physical libraries, has become the development goal and future trend of libraries [3]. In terms of resource construction, the fragmented management system and the outdated concept of solidification have seriously affected the development of digital libraries, making the coconstruction and sharing of document resources in digital libraries a difficult problem [4]. The digital information in the digital library is constantly changing in cycles of increase and decrease, and digital resources are constantly being created, edited, and described, marked and indexed, transmitted and acquired, and used and shared [5]. Various digital libraries try to meet the information needs of users by expanding their scales [6]. Due to technical barriers and practical barriers, the phenomenon of independent, isolated, and difficult to coordinate has formed, which aggravates the serious waste caused by the repeated construction of digital resources [7]. For a long time, digital resource island and digital resource overload are the main bottlenecks hindering the acquisition of digital library resources [8]. Digital library is a kind of digital information resources with a variety of media content, which can provide users with convenient, fast, and high-level information service mechanism. The island of digital resources is the difficulty in aggregation of digital resources caused by factors such as the distribution, diversity, industry division, and category classification of digital library resources, which also directly leads to difficulties in sharing digital resources and obtaining systematic knowledge production [9]. The most prominent feature of the isolated island of digital resources in digital library is the partial order and the overall disorder. Partial order refers to a single resource system that describes and analyzes resource information to varying degrees, and overall disorder refers to a semicompatible or incompatible state formed by numerous resource systems, resource objects, and resource content [10, 11]. Digital resource overload mainly refers to a state of information demand that cannot obtain the knowledge that users really need in the massive digital resources [12] and the ability to exploit the use and choice of resources [13]. The most prominent feature of digital resource overload is that the content of digital resources is overlapped and redundant, and the amount of redundant information is huge [14]. It draws lessons from the resource organization mode of the library, with the help of high and new technologies such as computer network communication, takes universal access to human knowledge as the goal, creatively uses knowledge classification and accurate retrieval methods, and effectively sorts information, so that people’s access to information and consumption is not limited by space and time to a large extent. In the face of massive information, users cannot obtain digital resources needed for practical use with high efficiency and high quality [15, 16]. Users quickly and accurately obtain the digital resources they need. Most of the current retrieval systems use keyword-based retrieval technology, and users have no technical barriers to use [17]. However, there are still isolated islands and overloading between the words actually detected by users and the digital resources they actually need. The drawbacks of information retrieval caused by the organization mechanism of digital resources are also very obvious [18]. Due to the polysemy and ambiguity of keywords, a single information retrieval and presentation based on keyword matching is difficult to meet the needs of users to obtain laws and patterns. The demand for knowledge content such as personalization cannot provide users with the semantic association between digital resources or knowledge [19]. From the perspective of users’ use, the aggregation of digital library resources should be guided by the needs of users, and the classification, aggregation, integration, and management of related resources should be carried out in a targeted manner. Improve user service quality. With the development and wide application of technology, users have new demands for the acquisition and utilization of digital resources [20–22]. Digital library will become the public information center and hub of the future society. Similarly, no matter what term is used in electronic library, virtual library, and digital library, digitalization is also the development direction of library. This new demand is mainly manifested in two aspects: on the one hand, in the way of demand, users hope to retrieve and obtain digital resources scattered in multiple heterogeneous resource systems at one time through the unified search portal and also to obtain related relationships. On the other hand, in terms of content requirements, users hope to find the required information in the massive digital resources, and more hope to obtain the knowledge content and knowledge logical relationship contained in these information, and hope that the system can connect knowledge and knowledge. The logical relationship can be presented and displayed in a relatively simple, clear, and easy-to-operate and easy-to-understand way, helping oneself understand, apply, and create knowledge. The improvement of user demand means the change from the demand for information to the demand for knowledge, and it is necessary to promote the development and optimization of the supply method of digital resources in digital libraries. Figure 1 is a histogram of the number of borrowed books.Figure 1
Histogram of the number of borrowed books.


## 2. State of the Art

### 2.1. Status Quo of Book Resource Management in the Information Age

#### 2.1.1. Lack of Perfect Management System

My country’s book resource management tasks have not yet established a standardized and scientific system, the overall planning of book management is incomplete, and no effective management and system measures have been introduced, resulting in the book management tasks of many enterprises and institutions. Due to the lack of a perfect management system, it will be difficult to carry out the task of retrieval, which has a negative effect on the work and school of enterprises and individuals. Therefore, it is necessary to improve the library resource management system.


#### 2.1.2. Lack of Professional Management Talents and Library Management Awareness

In the actual task of book resource management, managers generally lack management culture and quality, and enterprises and institutions have not paid enough attention to it, and there are relatively few truly high-quality talents related to book management in the society. The knowledge structure and professional ability of many librarians fail to adapt to the information society in time, thus affecting the quality and level of service for borrowers. And readers’ requirements for book types and service quality are gradually improving. If high-quality comprehensive management talents are not equipped, it will inevitably hinder the future development of book resource management.


#### 2.1.3. Lack of Coordination among Libraries

Most libraries pay more attention to the development of their own business level, and there are some cooperation projects between libraries. However, their coordination is not in place in some aspects. For example, the construction of databases still maintains their independence, resulting in resource, sharing becomes difficult, resulting in the underutilization of many book resources, and the overlapping of information resources will lead to waste. Digital library is not only a complete knowledge positioning system but also an information management model for the future development of the Internet. It can be widely used in the public information dissemination of social culture, lifelong education, mass media, business consulting, e-government, and other social organizations.The information-based book resource management, which is the core foundation of the library’s public services, is facing huge challenges. As a service place for further study and information consultation, the library bears the arduous task of providing humanized services for the recipients of information resources. Therefore, it is necessary to adapt to the basic development requirements of the information age and gradually build different types of integrated databases to meet the greatest needs of book borrowers.


### 2.2. Challenges in the Construction of Library Digital Resource Library

#### 2.2.1. Diversification of Information Resource Channels Poses Challenges to Libraries

In the era of “Internet+”, information technology has undergone tremendous changes. The channels for libraries to obtain information resources have increased greatly. They can obtain more and more information resources through open journals and knowledge bases. Open information resource bases and digital resource bases make the acquisition of information resources more and more convenient. In the context of the new era, readers can obtain information from the Internet and mobile sharing terminals through various retrieval tools. Libraries are no longer just a single source of information resources, which is gradually weakened. Statistical research on the current resource sources found that the library’s user visits and book lending showed a downward trend year by year, and the library’s resource construction has a long way to go. To sum up, the construction of the digital resource library of the library in the “Internet+” era needs to be gradually strengthened, not only to expand the traditional services but also to build a more comprehensive strategic deployment of the collection, by collecting and opening more information resources and integrating. The existing various resources build a digital resource library, enhance the popularity and competitiveness of the source of library resources, and gain a better reputation among users.


#### 2.2.2. Diversified User Demands Pose Challenges to the Allocation of Library Resources

The development of network communication and cloud computing is the most important technology in the “Internet+” era. The introduction of emerging technologies prompts libraries to improve the level of digital resource construction. Throughout most of the libraries at home and abroad, whether it is a public library or a university library, the procurement funds in recent years have gradually shifted to the purchase of digital resources, and the purchase of e-books is increasing day by day. In fact, due to the large differences in the majors of readers in libraries, liberal arts readers have a higher demand for paper documents, while science, engineering, and medical readers are more focused on the demand for digital resources. Since the basic knowledge of each major is still dominated by paper documents, as a library, in the current environment where the cost of paper documents is much higher than that of digital resources, considering the differences of users with different discipline backgrounds, by using limited resources, the purchase funds meet the personalized and diversified reading needs of different users. The purchase ratio of paper documents and digital resources should be reasonably allocated, the document storage structure of the collection should be optimized, and the advantages of cloud computing should be used to realize the parallel development of different disciplines and resource types.


#### 2.2.3. Emerging Procurement Models Pose Challenges to Library Business Processes

In the construction of library resource library, procurement, publication, and distribution are one of the key considerations, because these upstream industrial chains have also undergone great changes in the “Internet+” era. The overall situation of Chinese university librarians is that professional knowledge and skills generally cannot meet the requirements of the development of digital libraries. With the rise of digital library, the practical problems such as fewer professionals and technicians, lack of enthusiasm for work, and aging become more acute. With the continuous rise of technologies such as network transmission and cloud computing, traditional publishing and distribution technologies have been gradually eliminated. Instead, various emerging procurement models such as demand-driven and reader decision-making have been built through services such as booking volume and printing demand. In recent years, Wiley and other international academic institutions have also launched new evidence-based procurement models to solve the problem of procurement costs, reduce the time consumption of procurement, and achieve a win-win situation. Under the emerging procurement model, readers can select suitable literature resources according to the actual literature demand and add them to their personalized accounts, which greatly facilitate library readers and improve the personalization and efficiency of services. The emerging procurement methods also bring great challenges to the library construction process. Libraries should adjust the business process and build corresponding resource evaluation and collection standards.


## 2.1. Status Quo of Book Resource Management in the Information Age

### 2.1.1. Lack of Perfect Management System

My country’s book resource management tasks have not yet established a standardized and scientific system, the overall planning of book management is incomplete, and no effective management and system measures have been introduced, resulting in the book management tasks of many enterprises and institutions. Due to the lack of a perfect management system, it will be difficult to carry out the task of retrieval, which has a negative effect on the work and school of enterprises and individuals. Therefore, it is necessary to improve the library resource management system.


### 2.1.2. Lack of Professional Management Talents and Library Management Awareness

In the actual task of book resource management, managers generally lack management culture and quality, and enterprises and institutions have not paid enough attention to it, and there are relatively few truly high-quality talents related to book management in the society. The knowledge structure and professional ability of many librarians fail to adapt to the information society in time, thus affecting the quality and level of service for borrowers. And readers’ requirements for book types and service quality are gradually improving. If high-quality comprehensive management talents are not equipped, it will inevitably hinder the future development of book resource management.


### 2.1.3. Lack of Coordination among Libraries

Most libraries pay more attention to the development of their own business level, and there are some cooperation projects between libraries. However, their coordination is not in place in some aspects. For example, the construction of databases still maintains their independence, resulting in resource, sharing becomes difficult, resulting in the underutilization of many book resources, and the overlapping of information resources will lead to waste. Digital library is not only a complete knowledge positioning system but also an information management model for the future development of the Internet. It can be widely used in the public information dissemination of social culture, lifelong education, mass media, business consulting, e-government, and other social organizations.The information-based book resource management, which is the core foundation of the library’s public services, is facing huge challenges. As a service place for further study and information consultation, the library bears the arduous task of providing humanized services for the recipients of information resources. Therefore, it is necessary to adapt to the basic development requirements of the information age and gradually build different types of integrated databases to meet the greatest needs of book borrowers.


## 2.1.1. Lack of Perfect Management System

My country’s book resource management tasks have not yet established a standardized and scientific system, the overall planning of book management is incomplete, and no effective management and system measures have been introduced, resulting in the book management tasks of many enterprises and institutions. Due to the lack of a perfect management system, it will be difficult to carry out the task of retrieval, which has a negative effect on the work and school of enterprises and individuals. Therefore, it is necessary to improve the library resource management system.


## 2.1.2. Lack of Professional Management Talents and Library Management Awareness

In the actual task of book resource management, managers generally lack management culture and quality, and enterprises and institutions have not paid enough attention to it, and there are relatively few truly high-quality talents related to book management in the society. The knowledge structure and professional ability of many librarians fail to adapt to the information society in time, thus affecting the quality and level of service for borrowers. And readers’ requirements for book types and service quality are gradually improving. If high-quality comprehensive management talents are not equipped, it will inevitably hinder the future development of book resource management.


## 2.1.3. Lack of Coordination among Libraries

Most libraries pay more attention to the development of their own business level, and there are some cooperation projects between libraries. However, their coordination is not in place in some aspects. For example, the construction of databases still maintains their independence, resulting in resource, sharing becomes difficult, resulting in the underutilization of many book resources, and the overlapping of information resources will lead to waste. Digital library is not only a complete knowledge positioning system but also an information management model for the future development of the Internet. It can be widely used in the public information dissemination of social culture, lifelong education, mass media, business consulting, e-government, and other social organizations.The information-based book resource management, which is the core foundation of the library’s public services, is facing huge challenges. As a service place for further study and information consultation, the library bears the arduous task of providing humanized services for the recipients of information resources. Therefore, it is necessary to adapt to the basic development requirements of the information age and gradually build different types of integrated databases to meet the greatest needs of book borrowers.


## 2.2. Challenges in the Construction of Library Digital Resource Library

### 2.2.1. Diversification of Information Resource Channels Poses Challenges to Libraries

In the era of “Internet+”, information technology has undergone tremendous changes. The channels for libraries to obtain information resources have increased greatly. They can obtain more and more information resources through open journals and knowledge bases. Open information resource bases and digital resource bases make the acquisition of information resources more and more convenient. In the context of the new era, readers can obtain information from the Internet and mobile sharing terminals through various retrieval tools. Libraries are no longer just a single source of information resources, which is gradually weakened. Statistical research on the current resource sources found that the library’s user visits and book lending showed a downward trend year by year, and the library’s resource construction has a long way to go. To sum up, the construction of the digital resource library of the library in the “Internet+” era needs to be gradually strengthened, not only to expand the traditional services but also to build a more comprehensive strategic deployment of the collection, by collecting and opening more information resources and integrating. The existing various resources build a digital resource library, enhance the popularity and competitiveness of the source of library resources, and gain a better reputation among users.


### 2.2.2. Diversified User Demands Pose Challenges to the Allocation of Library Resources

The development of network communication and cloud computing is the most important technology in the “Internet+” era. The introduction of emerging technologies prompts libraries to improve the level of digital resource construction. Throughout most of the libraries at home and abroad, whether it is a public library or a university library, the procurement funds in recent years have gradually shifted to the purchase of digital resources, and the purchase of e-books is increasing day by day. In fact, due to the large differences in the majors of readers in libraries, liberal arts readers have a higher demand for paper documents, while science, engineering, and medical readers are more focused on the demand for digital resources. Since the basic knowledge of each major is still dominated by paper documents, as a library, in the current environment where the cost of paper documents is much higher than that of digital resources, considering the differences of users with different discipline backgrounds, by using limited resources, the purchase funds meet the personalized and diversified reading needs of different users. The purchase ratio of paper documents and digital resources should be reasonably allocated, the document storage structure of the collection should be optimized, and the advantages of cloud computing should be used to realize the parallel development of different disciplines and resource types.


### 2.2.3. Emerging Procurement Models Pose Challenges to Library Business Processes

In the construction of library resource library, procurement, publication, and distribution are one of the key considerations, because these upstream industrial chains have also undergone great changes in the “Internet+” era. The overall situation of Chinese university librarians is that professional knowledge and skills generally cannot meet the requirements of the development of digital libraries. With the rise of digital library, the practical problems such as fewer professionals and technicians, lack of enthusiasm for work, and aging become more acute. With the continuous rise of technologies such as network transmission and cloud computing, traditional publishing and distribution technologies have been gradually eliminated. Instead, various emerging procurement models such as demand-driven and reader decision-making have been built through services such as booking volume and printing demand. In recent years, Wiley and other international academic institutions have also launched new evidence-based procurement models to solve the problem of procurement costs, reduce the time consumption of procurement, and achieve a win-win situation. Under the emerging procurement model, readers can select suitable literature resources according to the actual literature demand and add them to their personalized accounts, which greatly facilitate library readers and improve the personalization and efficiency of services. The emerging procurement methods also bring great challenges to the library construction process. Libraries should adjust the business process and build corresponding resource evaluation and collection standards.


## 2.2.1. Diversification of Information Resource Channels Poses Challenges to Libraries

In the era of “Internet+”, information technology has undergone tremendous changes. The channels for libraries to obtain information resources have increased greatly. They can obtain more and more information resources through open journals and knowledge bases. Open information resource bases and digital resource bases make the acquisition of information resources more and more convenient. In the context of the new era, readers can obtain information from the Internet and mobile sharing terminals through various retrieval tools. Libraries are no longer just a single source of information resources, which is gradually weakened. Statistical research on the current resource sources found that the library’s user visits and book lending showed a downward trend year by year, and the library’s resource construction has a long way to go. To sum up, the construction of the digital resource library of the library in the “Internet+” era needs to be gradually strengthened, not only to expand the traditional services but also to build a more comprehensive strategic deployment of the collection, by collecting and opening more information resources and integrating. The existing various resources build a digital resource library, enhance the popularity and competitiveness of the source of library resources, and gain a better reputation among users.


## 2.2.2. Diversified User Demands Pose Challenges to the Allocation of Library Resources

The development of network communication and cloud computing is the most important technology in the “Internet+” era. The introduction of emerging technologies prompts libraries to improve the level of digital resource construction. Throughout most of the libraries at home and abroad, whether it is a public library or a university library, the procurement funds in recent years have gradually shifted to the purchase of digital resources, and the purchase of e-books is increasing day by day. In fact, due to the large differences in the majors of readers in libraries, liberal arts readers have a higher demand for paper documents, while science, engineering, and medical readers are more focused on the demand for digital resources. Since the basic knowledge of each major is still dominated by paper documents, as a library, in the current environment where the cost of paper documents is much higher than that of digital resources, considering the differences of users with different discipline backgrounds, by using limited resources, the purchase funds meet the personalized and diversified reading needs of different users. The purchase ratio of paper documents and digital resources should be reasonably allocated, the document storage structure of the collection should be optimized, and the advantages of cloud computing should be used to realize the parallel development of different disciplines and resource types.


## 2.2.3. Emerging Procurement Models Pose Challenges to Library Business Processes

In the construction of library resource library, procurement, publication, and distribution are one of the key considerations, because these upstream industrial chains have also undergone great changes in the “Internet+” era. The overall situation of Chinese university librarians is that professional knowledge and skills generally cannot meet the requirements of the development of digital libraries. With the rise of digital library, the practical problems such as fewer professionals and technicians, lack of enthusiasm for work, and aging become more acute. With the continuous rise of technologies such as network transmission and cloud computing, traditional publishing and distribution technologies have been gradually eliminated. Instead, various emerging procurement models such as demand-driven and reader decision-making have been built through services such as booking volume and printing demand. In recent years, Wiley and other international academic institutions have also launched new evidence-based procurement models to solve the problem of procurement costs, reduce the time consumption of procurement, and achieve a win-win situation. Under the emerging procurement model, readers can select suitable literature resources according to the actual literature demand and add them to their personalized accounts, which greatly facilitate library readers and improve the personalization and efficiency of services. The emerging procurement methods also bring great challenges to the library construction process. Libraries should adjust the business process and build corresponding resource evaluation and collection standards.


## 3. Methodology

How to deal with it and how to go deep into its interior to obtain useful information, researchers have made a lot of exploration, and people are no longer only on simple operations on data but also start to think and explore how to extract hidden data from massive data. The underlying knowledge or information provides powerful data support for the final decision, for example, the classification of library collections, the operation and management decisions of the book market, and the selection of books in the process of electronic library construction. Figure2 is a flow chart of data mining in the process of building a library resource base based on information technology.Figure 2
Data mining process.


### 3.1. Overview of FCM Clustering Algorithm

The FCM clustering algorithm is to dividen vectors xi (i=1,2,⋯,n) into c predetermined number of categories and find the fuzzy cluster center ci of each group, 1≤i≤c so that the dissimilar target function (Equation (4)) reaches the minimum threshold. The FCM clustering algorithm uses membership values between 0 and 1 to determine the degree to which each given data point xi belongs to each class. In addition to the normalization rule, the membership matrix U of a dataset needs to meet the following three conditions, namely:
(1)uij∈0,1,1⩽i⩽c,1⩽j⩽n,(2)∑i=1cuij=1,1⩽j⩽n,(3)0<∑j=1nuij<n,1⩽i⩽c.The general form of the dissimilarity objective function of the FCM clustering algorithm is as follows:(4)JU,c1,⋯,cc=∑i=1cJi=∑i=1c∑i=1nuijmdij2,where λj,j=1,⋯,n are the Lagrange multipliers of the n constraints of Equation (2). Derivative for all input parameters, the necessary condition to minimize Equation (4) is as follows:
(5)JU,c1,⋯,cc,λ1,⋯,λn=JU,c1,⋯,cc+∑j=1nλj∑i=1cuij−1=∑i=1c∑i=1nuijmdij2+∑i=1nλj∑i=1cuij−1,where uij is between 0 and 1, dij=ci−xj is the Euclidean distance between the i-th cluster center and the j-th data point, and m∈1,¥ is a weighting index. “Constructing the following new objective function” can obtain the necessary conditions for Equation (4) to reach the minimum value as follows:
(6)ci=∑j=1nuijmxj∑j=1nuijm,1⩽i⩽c,uij=1∑k=1cdij/dkj2/m−1,1⩽i⩽c,1⩽j⩽n.


### 3.2. Apriori Association Rule Mining

Association rule Apriori algorithm is the most classic algorithm in association rule mining technology, which can discover some hidden relationships between two things through a certain rule. Association rules can be represented byX⟶Y, where X represents the condition of the association rule and Y represents the continuation of the association rule. In addition, there are definitions of support and confidence in the association rule algorithm as follows:
(7)PAB=PABPB,where A and B represent different events. The correspondence between the support degree and the confidence degree can be calculated by the probability (Equation (8)).
(8)confYX=PYX=PXYPX≈NumberofXYoccurrencesNumberofXoccurrences.After setting the minimum support (Min_Sup) and minimum confidence (Min_Conf), the Apriori algorithm will read all transaction sets in the database.


### 3.3. FP_Growth Association Rule Mining

LetI=i1,i2,⋯,id be the set of all items in the music data and T=t1,t2,⋯,tN be the set of all transactions. The item sets contained in each transaction ti are subsets of I. In association analysis, the specific representations of support and confidence are as follows:
(9)sX⟶Y=σX∪YN,cX⟶Y=σX∪YσX,where N represents the number of transactions. Support is calculated as follows:
(10)s=xx∈D,rulei∈x,where D represents the training dataset and rulei represents the rules of D. Sets are considered itemsets in association analysis.


### 3.4. Introduction toK-Means Algorithm for Optimizing Initial Cluster Centers

The clustering algorithm evaluates the clustering effect by quantifying the distance between samples and the closeness of clusters. It is specifically expressed as follows: the smaller the distance between data within the same class, the better, and the larger the distance between different classes, the better. In this paper, the ratio of the difference value within the same cluster and the difference value between different clusters is defined as the standard for evaluating the quality of clustering effect. The sum of all intracluster distances in the dataset is defined asEin, which represents the sum of the distances between the data samples contained in each cluster and the center points of each cluster. The calculation formulas of intracluster distance and Ein are expressed as follows:
(11)Ein=∑x∈Ci1ni∑i=1kx−Z¯i2,where n is the number of all samples in the dataset, k is the number of classes, Ci is the i-th class cluster, x is the data samples in the i-th class, and Zi is the center point of the i-th class. The smaller the distance between clusters and the smaller the value of Ein, the more compact the data samples in all clusters, and the better the compactness of the clusters.In addition, it is also necessary to calculate the distance between the center points of each cluster as the distance between clusters. The distance between clusters is defined asEout, and the formula for calculating the distance between clusters Eout is as follows:
(12)Eout=1k∑i=1kCi−C¯2,where k represents the number of classes, that is, the number of clusters, Ci represents the cluster center point of the ith cluster, C represents the average value of the cluster centers of each cluster, and the larger the value of the distance Eout between clusters means the similarity between classes is small.The advantages and disadvantages of the clustering effect are evaluated by combining the two values of the distance within the cluster and the distance betweenEin and the cluster Eout. The smaller the ratio of intracluster distance and Ein to intercluster distance Eout, the better the clustering effect at this time. Using EE to express the evaluation standard of the degree of clustering effect, then EE is expressed as follows:
(13)EE=EinEout.From Equation (13), it can be concluded that the EE value gradually decreases first and then gradually increases. The smaller the EE value is, the better the clustering effect is. When the EE reaches the local minimum value, the clustering effect is the best. From this, the value of the number of clusters K can be determined.


## 3.1. Overview of FCM Clustering Algorithm

The FCM clustering algorithm is to dividen vectors xi (i=1,2,⋯,n) into c predetermined number of categories and find the fuzzy cluster center ci of each group, 1≤i≤c so that the dissimilar target function (Equation (4)) reaches the minimum threshold. The FCM clustering algorithm uses membership values between 0 and 1 to determine the degree to which each given data point xi belongs to each class. In addition to the normalization rule, the membership matrix U of a dataset needs to meet the following three conditions, namely:
(1)uij∈0,1,1⩽i⩽c,1⩽j⩽n,(2)∑i=1cuij=1,1⩽j⩽n,(3)0<∑j=1nuij<n,1⩽i⩽c.The general form of the dissimilarity objective function of the FCM clustering algorithm is as follows:(4)JU,c1,⋯,cc=∑i=1cJi=∑i=1c∑i=1nuijmdij2,where λj,j=1,⋯,n are the Lagrange multipliers of the n constraints of Equation (2). Derivative for all input parameters, the necessary condition to minimize Equation (4) is as follows:
(5)JU,c1,⋯,cc,λ1,⋯,λn=JU,c1,⋯,cc+∑j=1nλj∑i=1cuij−1=∑i=1c∑i=1nuijmdij2+∑i=1nλj∑i=1cuij−1,where uij is between 0 and 1, dij=ci−xj is the Euclidean distance between the i-th cluster center and the j-th data point, and m∈1,¥ is a weighting index. “Constructing the following new objective function” can obtain the necessary conditions for Equation (4) to reach the minimum value as follows:
(6)ci=∑j=1nuijmxj∑j=1nuijm,1⩽i⩽c,uij=1∑k=1cdij/dkj2/m−1,1⩽i⩽c,1⩽j⩽n.


## 3.2. Apriori Association Rule Mining

Association rule Apriori algorithm is the most classic algorithm in association rule mining technology, which can discover some hidden relationships between two things through a certain rule. Association rules can be represented byX⟶Y, where X represents the condition of the association rule and Y represents the continuation of the association rule. In addition, there are definitions of support and confidence in the association rule algorithm as follows:
(7)PAB=PABPB,where A and B represent different events. The correspondence between the support degree and the confidence degree can be calculated by the probability (Equation (8)).
(8)confYX=PYX=PXYPX≈NumberofXYoccurrencesNumberofXoccurrences.After setting the minimum support (Min_Sup) and minimum confidence (Min_Conf), the Apriori algorithm will read all transaction sets in the database.


## 3.3. FP_Growth Association Rule Mining

LetI=i1,i2,⋯,id be the set of all items in the music data and T=t1,t2,⋯,tN be the set of all transactions. The item sets contained in each transaction ti are subsets of I. In association analysis, the specific representations of support and confidence are as follows:
(9)sX⟶Y=σX∪YN,cX⟶Y=σX∪YσX,where N represents the number of transactions. Support is calculated as follows:
(10)s=xx∈D,rulei∈x,where D represents the training dataset and rulei represents the rules of D. Sets are considered itemsets in association analysis.


## 3.4. Introduction toK-Means Algorithm for Optimizing Initial Cluster Centers

The clustering algorithm evaluates the clustering effect by quantifying the distance between samples and the closeness of clusters. It is specifically expressed as follows: the smaller the distance between data within the same class, the better, and the larger the distance between different classes, the better. In this paper, the ratio of the difference value within the same cluster and the difference value between different clusters is defined as the standard for evaluating the quality of clustering effect. The sum of all intracluster distances in the dataset is defined asEin, which represents the sum of the distances between the data samples contained in each cluster and the center points of each cluster. The calculation formulas of intracluster distance and Ein are expressed as follows:
(11)Ein=∑x∈Ci1ni∑i=1kx−Z¯i2,where n is the number of all samples in the dataset, k is the number of classes, Ci is the i-th class cluster, x is the data samples in the i-th class, and Zi is the center point of the i-th class. The smaller the distance between clusters and the smaller the value of Ein, the more compact the data samples in all clusters, and the better the compactness of the clusters.In addition, it is also necessary to calculate the distance between the center points of each cluster as the distance between clusters. The distance between clusters is defined asEout, and the formula for calculating the distance between clusters Eout is as follows:
(12)Eout=1k∑i=1kCi−C¯2,where k represents the number of classes, that is, the number of clusters, Ci represents the cluster center point of the ith cluster, C represents the average value of the cluster centers of each cluster, and the larger the value of the distance Eout between clusters means the similarity between classes is small.The advantages and disadvantages of the clustering effect are evaluated by combining the two values of the distance within the cluster and the distance betweenEin and the cluster Eout. The smaller the ratio of intracluster distance and Ein to intercluster distance Eout, the better the clustering effect at this time. Using EE to express the evaluation standard of the degree of clustering effect, then EE is expressed as follows:
(13)EE=EinEout.From Equation (13), it can be concluded that the EE value gradually decreases first and then gradually increases. The smaller the EE value is, the better the clustering effect is. When the EE reaches the local minimum value, the clustering effect is the best. From this, the value of the number of clusters K can be determined.


## 4. Result Analysis and Discussion

### 4.1. Experimental Data and Environment

The hardware environment is Intel Core i5 2.8 GHz processor, 4 G memory, and 500 G hard disk. The software environment is Windows 10 operating system, MATLAB 7.0 simulation software. The experiment uses the dataset in the library management system of a university. The dataset contains 10,628 books borrowing history records of the library in 2018, as shown in Table1.Table 1
Book lending history (part).
Borrowing card numberISBN numberBorrowing card numberISBN number03732103-28337-203738115-17762-803733221-12434-303739106-62588-503734106-41198-703740142-82207-203735115-18626-303741201-42897-603736103-12134-403742182-10125-703737152-41523-603743182-06675-2…………First, data generalization should be performed on the book borrowing history data. According to the settings of the library management system in the university, all the books in the dataset are divided into six categories, namely, E (computer), H (English), W (automatic), A (art), C (social category), and J (literature category), and the data generalization of these six categories of books is shown in Table2.Table 2
Data generalization of 6 types of books.
Borrowing card numberISBN numberBorrowing card numberISBN number03732J, E, W03738C, H03733C, A, H03739A, J, W03734J, C, H03740W, E, H03735E, W03741W, H03736E, H, A03742A, H03737E, A03743J, H…………


### 4.2. Experimental Results and Analysis

As shown in Figure3, for different support degrees, the execution efficiency of the BTA algorithm is higher than that of the Apriori algorithm. Moreover, the execution efficiency advantage of the BTA algorithm is more obvious when the support degree is small.Figure 3
Time overhead of two algorithms under different support thresholds.The standard FP-growth algorithm, the Apriori algorithm, and the improved FP-growth algorithm are compared, and the number of association rules mined under different support degrees is compared. The results are shown in Figure4, where the minimum confidence degree minconf=35% is set. Figure 4 shows the number of association rules mined by the three algorithms when the database remains unchanged, as the minimum support threshold gradually increases. The other two algorithms have a lot more, because the improved FP-growth algorithm realizes multilayer association rule mining, and the subdatabase division avoids the loss of some fuzzy attributes caused by excessive compression; that is, the mining results contain more information quantity.Figure 4
Comparison of the number of association rules obtained by the three algorithms under different minimum support degrees.Figure5 shows the comparison of the running time of the three algorithms when the database remains unchanged, as the minimum support threshold gradually increases. It can be seen that with the increase of the minimum support degree, the running time of the three algorithms shows a decreasing trend, but the FP-growth algorithm and the algorithm in this paper have obvious performance advantages when the support degree is small. This is because compared to the Apriori algorithm, the two algorithms scan the database significantly less, only 2 times.Figure 5
Comparison of operating efficiency obtained by three algorithms under different minimum support degrees.Figure6 shows the comparison of the running time of the three algorithms when the number of transactions in the book database increases with the fixed minimum support of 0.1. It can be seen that as the number of transactions increases, the execution time of the three algorithms increases gradually. However, the execution time of the improved FP-growth algorithm in this paper is shorter than that of the standard FP-growth and Apriori algorithms. That is to say, the improved FP-growth algorithm in this paper shows a strong efficiency advantage.Figure 6
Comparison of operating efficiencies obtained by the three algorithms under different transaction numbers.This is because when the minimum support threshold is small, the number of frequent 1-itemsets that meet the conditional threshold is relatively large, resulting in an increase in I/O resources and memory usage. The improved FP-growth algorithm is based on the first item. The transaction builds the sub-FP-tree structure of the sub-database, which avoids excessively increasing the space complexity of the FP-tree structure and enables asynchronous execution. At the same time, the parent items that are not frequent items in the hierarchical tree are filtered out in real time to reduce the scanning space, which is beneficial to reduce the occupation of I/O resources, thereby improving the mining efficiency of large databases.As shown in Figure7, as the total number of books in the library management system continues to increase, the accuracy obtained by the three algorithms continues to improve. Among them, the improvement speed of LDA algorithm is the slowest, the improvement speed of traditional association rule algorithm is second, and the improvement speed of the method proposed in this paper is the fastest. The validity and feasibility of the algorithm proposed in this paper are verified, which can effectively complete user book recommendation, and under the same conditions, the algorithm proposed in this paper has higher accuracy.Figure 7
Accuracy comparison of three different algorithms.


## 4.1. Experimental Data and Environment

The hardware environment is Intel Core i5 2.8 GHz processor, 4 G memory, and 500 G hard disk. The software environment is Windows 10 operating system, MATLAB 7.0 simulation software. The experiment uses the dataset in the library management system of a university. The dataset contains 10,628 books borrowing history records of the library in 2018, as shown in Table1.Table 1
Book lending history (part).
Borrowing card numberISBN numberBorrowing card numberISBN number03732103-28337-203738115-17762-803733221-12434-303739106-62588-503734106-41198-703740142-82207-203735115-18626-303741201-42897-603736103-12134-403742182-10125-703737152-41523-603743182-06675-2…………First, data generalization should be performed on the book borrowing history data. According to the settings of the library management system in the university, all the books in the dataset are divided into six categories, namely, E (computer), H (English), W (automatic), A (art), C (social category), and J (literature category), and the data generalization of these six categories of books is shown in Table2.Table 2
Data generalization of 6 types of books.
Borrowing card numberISBN numberBorrowing card numberISBN number03732J, E, W03738C, H03733C, A, H03739A, J, W03734J, C, H03740W, E, H03735E, W03741W, H03736E, H, A03742A, H03737E, A03743J, H…………


## 4.2. Experimental Results and Analysis

As shown in Figure3, for different support degrees, the execution efficiency of the BTA algorithm is higher than that of the Apriori algorithm. Moreover, the execution efficiency advantage of the BTA algorithm is more obvious when the support degree is small.Figure 3
Time overhead of two algorithms under different support thresholds.The standard FP-growth algorithm, the Apriori algorithm, and the improved FP-growth algorithm are compared, and the number of association rules mined under different support degrees is compared. The results are shown in Figure4, where the minimum confidence degree minconf=35% is set. Figure 4 shows the number of association rules mined by the three algorithms when the database remains unchanged, as the minimum support threshold gradually increases. The other two algorithms have a lot more, because the improved FP-growth algorithm realizes multilayer association rule mining, and the subdatabase division avoids the loss of some fuzzy attributes caused by excessive compression; that is, the mining results contain more information quantity.Figure 4
Comparison of the number of association rules obtained by the three algorithms under different minimum support degrees.Figure5 shows the comparison of the running time of the three algorithms when the database remains unchanged, as the minimum support threshold gradually increases. It can be seen that with the increase of the minimum support degree, the running time of the three algorithms shows a decreasing trend, but the FP-growth algorithm and the algorithm in this paper have obvious performance advantages when the support degree is small. This is because compared to the Apriori algorithm, the two algorithms scan the database significantly less, only 2 times.Figure 5
Comparison of operating efficiency obtained by three algorithms under different minimum support degrees.Figure6 shows the comparison of the running time of the three algorithms when the number of transactions in the book database increases with the fixed minimum support of 0.1. It can be seen that as the number of transactions increases, the execution time of the three algorithms increases gradually. However, the execution time of the improved FP-growth algorithm in this paper is shorter than that of the standard FP-growth and Apriori algorithms. That is to say, the improved FP-growth algorithm in this paper shows a strong efficiency advantage.Figure 6
Comparison of operating efficiencies obtained by the three algorithms under different transaction numbers.This is because when the minimum support threshold is small, the number of frequent 1-itemsets that meet the conditional threshold is relatively large, resulting in an increase in I/O resources and memory usage. The improved FP-growth algorithm is based on the first item. The transaction builds the sub-FP-tree structure of the sub-database, which avoids excessively increasing the space complexity of the FP-tree structure and enables asynchronous execution. At the same time, the parent items that are not frequent items in the hierarchical tree are filtered out in real time to reduce the scanning space, which is beneficial to reduce the occupation of I/O resources, thereby improving the mining efficiency of large databases.As shown in Figure7, as the total number of books in the library management system continues to increase, the accuracy obtained by the three algorithms continues to improve. Among them, the improvement speed of LDA algorithm is the slowest, the improvement speed of traditional association rule algorithm is second, and the improvement speed of the method proposed in this paper is the fastest. The validity and feasibility of the algorithm proposed in this paper are verified, which can effectively complete user book recommendation, and under the same conditions, the algorithm proposed in this paper has higher accuracy.Figure 7
Accuracy comparison of three different algorithms.


## 5. Conclusion

The construction of digital library is a huge, systematic, and long-term project. The purchase of hardware equipment and software resources, network wiring engineering, personnel training, the renewal of digital resources, the digital conversion of library collections, and so on all need sufficient funds for support, but insufficient funds are a chronic problem that puzzles the development of university libraries. With the characteristics of category attributes and mixed attributes, the fuzzy clustering algorithm is combined with attribute weighted optimization, and then, the optimization iterative formula is derived and the weighted clustering algorithm is formed to cluster and analyze the relevant influencing factors in the management of book data. The FP_Growth algorithm in the association rule mining algorithm has been improved to improve the operation efficiency of association rule mining, so it is more suitable for mining tasks in large databases. The simulation results on a large-scale book database show that compared with the Apriori algorithm and the standard FP-growth algorithm, the improved FP-growth association rule algorithm proposed has a significant improvement in operating efficiency, which provides a scientific basis for book recommendation. However, the improved algorithm in this paper will still recursively generate a large number of conditional FP-Trees, and in the process of filtering out the hierarchical tree, useful rules, such as user interests, may be filtered out. Filter out conditions for in-depth analysis to further improve accuracy and performance.



---

*Source: 5359767-2022-08-19.xml*