bluuebunny/crossref_metadata_2025 huggingface dataset:
DFM as a Conceptual Model for Data Warehouse
Authors: Golfarelli Matteo
Published: May 2011

Conceptual modeling is widely recognized to be the necessary foundation for building a database that is well-documented and fully satisfies the user requirements. In particular, from the designer point of view the availability of a conceptual model provides a higher level of abstraction in describing the warehousing process and its architecture in all its aspects. Typically conceptual models rely on a graphical notation that facilitates writing, understanding, and managing conceptual schemata by both designers and users. The Entity/Relationship (E/R) model (Chen, 1976) is widespread in the enterprises as a conceptual formalism to provide standard documentation for relational information systems; nevertheless, as E/R is oriented to support queries that navigate associations between data rather than synthesize them, it is not well-suited for data warehousing (Kimball, 1998). Actually, the E/R model has enough expressivity to represent most concepts necessary for modeling a Data Warehouse (DW); on the other hand, in its basic form, it is not able to properly emphasize the key aspects of the multidimensional model, so that its usage for DWs is expensive from the point of view of the graphical notation and not intuitive (Rizzi, 2006). Some designers claim that star schemata are expressive enough for conceptual modeling. Actually, a star schema is just a (denormalized) relational schema, so it merely defines a set of relations and integrity constraints. Using star schema for conceptual modeling is like starting to build a complex software by writing the code, without the support of any static, functional, or dynamic model, which typically leads to very poor results from the points of view of adherence to user requirements, maintenance, and reuse. For all these reasons, in the last few years the research literature has proposed several original approaches for modeling a DW, some based on extensions of known conceptual formalisms (e.g. E/R, Unified Modeling Language (UML)), some based on ad hoc ones. Remarkably, a comparison of the different models made by Abello (2006) pointed out that, abstracting from their graphical form, the core expressivity is similar, thus proving that the academic community reached an informal agreement on the required expressivity. This paper discusses the expressivity of an ad hoc conceptual model, the Dimensional Fact Model (DFM), in order to let the user verify the usefulness of a conceptual modeling step in DW design. After a brief listing of the main conceptual model proposals, the basic and advanced features in DW conceptual modeling are introduced and described by examples. Finally, the current trends in DW conceptual modeling are reported and the conclusions are drawn.
Enhancing Web Search through Web Structure Mining
Authors: Wen Ji-Rong
Published: May 2011

The Web is an open and free environment for people to publish and get information. Everyone on the Web can be either an author, a reader, or both. The language of the Web, HTML (Hypertext Markup Language), is mainly designed for information display, not for semantic representation. Therefore, current Web search engines usually treat Web pages as unstructured documents, and traditional information retrieval (IR) technologies are employed for Web page parsing, indexing, and searching. The unstructured essence of Web pages seriously blocks more accurate search and advanced applications on the Web. For example, many sites contain structured information about various products. Extracting and integrating product information from multiple Web sites could lead to powerful search functions, such as comparison shopping and business intelligence. However, these structured data are embedded in Web pages, and there are no proper traditional methods to extract and integrate them. Another example is the link structure of the Web. If used properly, information hidden in the links could be taken advantage of to effectively improve search performance and make Web search go beyond traditional information retrieval (Page, Brin, Motwani, & Winograd, 1998, Kleinberg, 1998). Although XML (Extensible Markup Language) is an effort to structuralize Web data by introducing semantics into tags, it is unlikely that common users are willing to compose Web pages using XML due to its complication and the lack of standard schema definitions. Even if XML is extensively adopted, a huge amount of pages are still written in the HTML format and remain unstructured. Web structure mining is the class of methods to automatically discover structured data and information from the Web. Because the Web is dynamic, massive and heterogeneous, automated Web structure mining calls for novel technologies and tools that may take advantage of state-of-the-art technologies from various areas, including machine learning, data mining, information retrieval, and databases and natural language processing.
Evolutionary Development of ANNs for Data Mining
Authors: Rivero Daniel
Published: May 2011

Artificial Neural Networks (ANNs) are learning systems from the Artificial Intelligence (AI) world that have been used for solving complex problems related to different aspects as classification, clustering, or regression (Haykin, 1999), although they have been specially used in Data Mining. These systems are, due to their interesting characteristics, powerful techniques used by the researchers in different environments (Rabuñal, 2005). Nevertheless, the use of ANNs implies certain problems, mainly related to their development processes. The development of ANNs can be divided into two parts: architecture development and training and validation. The architecture development determines not only the number of neurons of the ANN, but also the type of the connections among those neurons. The training will determine the connection weights for such architecture. Traditionally, and given that the architecture of the network depends on the problem to be solved, the architecture design process is usually performed by the use of a manual process, meaning that the expert has to test different architectures to find the one able to achieve the best results. Therefore, the expert must perform various tests for training different architectures in order to determine which one of these architectures is the best one. This is a slow process due to the fact that architecture determination is a manual process, although techniques for relatively automatic creation of ANNs have been recently developed. This work presents various techniques for the development of ANNs, so that there would be needed much less human participation for such development.
On Explanation-Oriented Data Mining
Authors: Yao Yiyu
Published: May 2011

The objective of data mining is to discover new and useful knowledge, in order to gain a better understanding of nature. This in fact is the goal of scientists when carrying out scientific research, independent in their various disciplines. This goal-oriented view enables us to re-examine data mining in a wider context of scientific research. The consequence after the immediate comparison between scientific research and data mining is that, an explanation discovery and evaluation task is added to the existing data mining framework. In this chapter, we elaborate the basic concerns and methods of explanation discovery and evaluation. Explanationoriented association mining is employed as a concrete example to show the whole framework.
Extending a Conceptual Multidimensional Model for Representing Spatial Data
Authors: Malinowski Elzbieta , Zimányi Esteban
Published: May 2011

Data warehouses keep large amounts of historical data in order to help users at different management levels to make more effective decisions. Conventional data warehouses are designed based on a multidimensional view of data. They are usually represented as star or snowflake schemas that contain relational tables called fact and dimension tables. A fact table expresses the focus of analysis (e.g., analysis of sales) and contains numeric data called measures (e.g., quantity). Measures can be analyzed according to different analysis criteria or dimensions (e.g., by product). Dimensions include attributes that can form hierarchies (e.g., product-category). Data in a data warehouse can be dynamically manipulated using on-line analysis processing (OLAP) systems. In particular, these systems allow automatic measure aggregations while traversing hierarchies. For example, the roll-up operation transforms detailed measures into aggregated data (e.g., daily into monthly sales) while the drill-down operation does the contrary. Data warehouses typically include a location dimension, e.g., store or client address. This dimension is usually represented in an alphanumeric format. However, the advantages of using spatial data in the analysis process are well known since visualizing data in space allows users to reveal patterns that are difficult to discover otherwise. Spatial databases have been used for several decades for storing and managing spatial data. This kind of data typically represents geographical objects, i.e., objects located on the Earth’s surface (such as mountains, cities) or geographic phenomena (such as temperature, altitude). Due to technological advances, the amount of available spatial data is growing considerably, e.g., satellite images, and location data from remote sensing systems, such as Global Positioning Systems (GPS). Spatial databases are typically used for daily business manipulations, e.g., to find a specific place from the current position given by a GPS. However, spatial databases are not well suited for supporting the decision-making process (Bédard, Rivest, & Proulx, 2007), e.g., to find the best location for a new store. Therefore, the field of spatial data warehouses emerged as a response to the necessity of analyzing high volumes of spatial data. Since applications including spatial data are usually complex, they should be modeled at a conceptual level taking into account users’ requirements and leaving out complex implementation details. The advantages of using conceptual models for database design are well known. In conventional data warehouses, a multidimensional model is commonly used for expressing users’ requirements and for facilitating the subsequent implementation; however, in spatial data warehouses this model is seldom used. Further, existing conceptual models for spatial databases are not adequate for multidimensional modeling since they do not include the concepts of dimensions, hierarchies, and measures.
Facial Recognition
Authors: Lewis Rory A. , Ras Zbigniew W.
Published: May 2011

Over the past decade Facial Recognition has become more cohesive and reliable than ever before. We begin with an analysis explaining why certain facial recognition methodologies examined under FERET, FRVT 2000, FRVT 2002, and FRVT 2006 have become stronger and why other approaches to facial recognition are losing traction. Second, we cluster the stronger approaches in terms of what approaches are mutually inclusive or exclusive to surrounding methodologies. Third, we discuss and compare emerging facial recognition technology in light of the aforementioned clusters. In conclusion, we suggest a road map that takes into consideration the final goals of each cluster, that given each clusters weakness, will make it easier to combine methodologies with surrounding clusters.
Feature Extraction/Selection in High-Dimensional Spectral Data
Authors: Kim Seoung Bum
Published: May 2011

Development of advanced sensing technology has multiplied the volume of spectral data, which is one of the most common types of data encountered in many research fields that require advanced mathematical methods with highly efficient computation. Examples of the fields in which spectral data abound include nearinfrared, mass spectroscopy, magnetic resonance imaging, and nuclear magnetic resonance spectroscopy. The introduction of a variety of spectroscopic techniques makes it possible to investigate changes in composition in a spectrum and to quantify them without complex preparation of samples. However, a major limitation in the analysis of spectral data lies in the complexity of the signals generated by the presence of a large number of correlated features. Figure 1 displays a high-level diagram of the overall process of modeling and analyzing spectral data. The collected spectra should be first preprocessed to ensure high quality data. Preprocessing steps generally include denoising, baseline correction, alignment, and normalization. Feature extraction/selection identifies the important features for prediction, and relevant models are constructed through the learning processes. The feedback path from the results of the validation step enables control and optimization of all previous steps. Explanatory analysis and visualization can provide initial guidelines that make the subsequent steps more efficient. This chapter focuses on the feature extraction/selection step in the modeling and analysis of spectral data. Particularly, throughout the chapter, the properties of feature extraction/selection procedures are demonstrated with spectral data from high-resolution nuclear magnetic resonance spectroscopy, one of the widely used techniques for studying metabolomics.
Frequent Sets Mining in Data Stream Environments
Authors: Dang Xuan Hong , Ng Wee-Keong , Ong Kok-Leong , Lee Vincent
Published: May 2011

In recent years, data streams have emerged as a new data type that has attracted much attention from the data mining community. They arise naturally in a number of applications (Brian et al., 2002), including financial service (stock ticker, financial monitoring), sensor networks (earth sensing satellites, astronomic observations), web tracking and personalization (webclick streams). These stream applications share three distinguishing characteristics that limit the applicability of most traditional mining algorithms (Minos et al., 2002; Pedro and Geoff, 2001): (1) the continuous arrival rate of the stream is high and unpredictable; (2) the volume of data is unbounded, making it impractical to store the entire content of the stream; (3) in terms of practical applicability, stream mining results are often expected to be closely approximated the exact results as well as to be available at any time. Consequently, the main challenge in mining data streams is to develop effective algorithms that support the processing of stream data in one-pass manner (preferably on-line) whilst operating under system resources limitations (e.g., memory space, CPU cycles or bandwidth). This chapter discusses the above challenge in the context of finding frequent sets from transactional data streams. The problems will be presented and some effective methods, both from deterministic and probabilistic approaches, are reviewed in details. The tradeoffs between memory space and accuracy of mining results are also discussed. Furthermore, the problems will be considered in three fundamental mining models for stream environments: landmark window, forgetful window and sliding window models.
Guided Sequence Alignment
Authors: Arslan Abdullah N.
Published: May 2011

Sequence alignment is one of the most fundamental problems in computational biology. Ordinarily, the problem aims to align symbols of given sequences in a way to optimize similarity score. This score is computed using a given scoring matrix that assigns a score to every pair of symbols in an alignment. The expectation is that scoring matrices perform well for alignments of all sequences. However, it has been shown that this is not always true although scoring matrices are derived from known similarities. Biological sequences share common sequence structures that are signatures of common functions, or evolutionary relatedness. The alignment process should be guided by constraining the desired alignments to contain these structures even though this does not always yield optimal scores. Changes in biological sequences occur over the course of millions of years, and in ways, and orders we do not completely know. Sequence alignment has become a dynamic area where new knowledge is acquired, new common structures are extracted from sequences, and these yield more sophisticated alignment methods, which in turn yield more knowledge. This feedback loop is essential for this inherently difficult task. The ordinary definition of sequence alignment does not always reveal biologically accurate similarities. To overcome this, there have been attempts that redefined sequence similarity. Huang (1994) proposed an optimization problem in which close matches are rewarded more favorably than the same number of isolated matches. Zhang, Berman & Miller (1998) proposed an algorithm that finds alignments free of low scoring regions. Arslan, Egecioglu, & Pevzner (2001) proposed length-normalized local sequence alignment for which the objective is to find subsequences that yield maximum length-normalized score where the length-normalized score of a given alignment is its score divided by sum of subsequence-lengths involved in the alignment. This can be considered as a contextdependent sequence alignment where a high degree of local similarity defines a context. Arslan, Egecioglu, & Pevzner (2001) presented a fractional programming algorithm for the resulting problem. Although these attempts are important, some biologically meaningful alignments can contain motifs whose inclusions are not guaranteed in the alignments returned by these methods. Our emphasis in this chapter is on methods that guide sequence alignment by requiring desired alignments to contain given common structures identified in sequences (motifs).
Histograms for OLAP and Data-Stream Queries
Authors: Buccafurri Francesco
Published: May 2011

Histograms are an important tool for data reduction both in the field of data-stream querying and in OLAP, since they allow us to represent large amount of data in a very compact structure, on which both efficient mining techniques and OLAP queries can be executed. Significant time- and memory-cost advantages may derive from data reduction, but the trade-off with the accuracy has to be managed in order to obtain considerable improvements of the overall capabilities of mining and OLAP tools. In this chapter we focus on histograms, that are shown in the recent literature to be one of the possible concrete answers to the above requirements.
Incremental Mining from News Streams
Authors: Chung Seokkyung
Published: May 2011

With the rapid growth of the World Wide Web, Internet users are now experiencing overwhelming quantities of online information. Since manually analyzing the data becomes nearly impossible, the analysis would be performed by automatic data mining techniques to fulfill users’ information needs quickly. On most Web pages, vast amounts of useful knowledge are embedded into text. Given such large sizes of text collection, mining tools, which organize the text datasets into structured knowledge, would enhance efficient document access. This facilitates information search and, at the same time, provides an efficient framework for document repository management as the number of documents becomes extremely huge. Given that the Web has become a vehicle for the distribution of information, many news organizations are providing newswire services through the Internet. Given this popularity of the Web news services, text mining on news datasets has received significant attentions during the past few years. In particular, as several hundred news stories are published everyday at a single Web news site, triggering the whole mining process whenever a document is added to the database is computationally impractical. Therefore, efficient incremental text mining tools need to be developed.
Information Fusion for Scientific Literature Classification
Authors: Yen Gary G.
Published: May 2011

Scientific literatures can be organized to serve as a roadmap for researchers by pointing where and when the scientific community has been and is heading to. They present historic and current state-of-the-art knowledge in the interesting areas of study. They also document valuable information including author lists, affiliated institutions, citation information, keywords, etc., which can be used to extract further information that will assist in analyzing their content and relationship with one another. However, their tremendously growing size and the increasing diversity of research fields have become a major concern, especially for organization, analysis, and exploration of such documents. This chapter proposes an automatic scientific literature classification method (ASLCM) that makes use of different information extracted from the literatures to organize and present them in a structured manner. In the proposed ASLCM, multiple similarity information is extracted from all available sources and fused to give an optimized and more meaningful classification through using a genetic algorithm. The final result is used to identify the different research disciplines within the collection, their emergence and termination, major collaborators, centers of excellence, their influence, and the flow of information among the multidisciplinary research areas.
Integrative Data Analysis for Biological Discovery
Authors: Moturu Sai
Published: May 2011

As John Muir noted, “When we try to pick out anything by itself, we find it hitched to everything else in the Universe” (Muir, 1911). In tune with Muir’s elegantly stated notion, research in molecular biology is progressing toward a systems level approach, with a goal of modeling biological systems at the molecular level. To achieve such a lofty goal, the analysis of multiple datasets is required to form a clearer picture of entire biological systems (Figure 1). Traditional molecular biology studies focus on a specific process in a complex biological system. The availability of high-throughput technologies allows us to sample tens of thousands of features of biological samples at the molecular level. Even so, these are limited to one particular view of a biological system governed by complex relationships and feedback mechanisms on a variety of levels. Integrated analysis of varied biological datasets from the genetic, translational, and protein levels promises more accurate and comprehensive results, which help discover concepts that cannot be found through separate, independent analyses. With this article, we attempt to provide a comprehensive review of the existing body of research in this domain.
Learning Kernels for Semi-Supervised Clustering
Authors: Yan Bojun
Published: May 2011

As a recent emerging technique, semi-supervised clustering has attracted significant research interest. Compared to traditional clustering algorithms, which only use unlabeled data, semi-supervised clustering employs both unlabeled and supervised data to obtain a partitioning that conforms more closely to the user’s preferences. Several recent papers have discussed this problem (Cohn, Caruana, & McCallum, 2003; Bar- Hillel, Hertz, Shental, & Weinshall, 2003; Xing, Ng, Jordan, & Russell, 2003; Basu, Bilenko, & Mooney, 2004; Kulis, Dhillon, & Mooney, 2005). In semi-supervised clustering, limited supervision is provided as input. The supervision can have the form of labeled data or pairwise constraints. In many applications it is natural to assume that pairwise constraints are available (Bar-Hillel, Hertz, Shental, & Weinshall, 2003; Wagstaff, Cardie, Rogers, & Schroedl, 2001). For example, in protein interaction and gene expression data (Segal, Wang, & Koller, 2003), pairwise constraints can be derived from the background domain knowledge. Similarly, in information and image retrieval, it is easy for the user to provide feedback concerning a qualitative measure of similarity or dissimilarity between pairs of objects. Thus, in these cases, although class labels may be unknown, a user can still specify whether pairs of points belong to the same cluster (Must-Link) or to different ones (Cannot-Link). Furthermore, a set of classified points implies an equivalent set of pairwise constraints, but not vice versa. Recently, a kernel method for semi-supervised clustering has been introduced (Kulis, Dhillon, & Mooney, 2005). This technique extends semi-supervised clustering to a kernel space, thus enabling the discovery of clusters with non-linear boundaries in input space. While a powerful technique, the applicability of a kernel-based semi-supervised clustering approach is limited in practice, due to the critical settings of kernel’s parameters. In fact, the chosen parameter values can largely affect the quality of the results. While solutions have been proposed in supervised learning to estimate the optimal kernel’s parameters, the problem presents open challenges when no labeled data are provided, and all we have available is a set of pairwise constraints.
Mining Chat Discussions
Authors: Daniel Licthnow Stanley Loh , Tiago Primo Thyago Borges
Published: May 2011

According to Nonaka & Takeuchi (1995), the majority of the organizational knowledge comes from interactions between people. People tend to reuse solutions from other persons in order to gain productivity. When people communicate to exchange information or acquire knowledge, the process is named Collaboration. Collaboration is one of the most important tasks for innovation and competitive advantage within learning organizations (Senge, 2001). It is important to record knowledge to later reuse and analysis. If knowledge is not adequately recorded, organized and retrieved, the consequence is re-work, low productivity and lost of opportunities. Collaboration may be realized through synchronous interactions (e.g., exchange of messages in a chat), asynchronous interactions (e.g., electronic mailing lists or forums), direct contact (e.g., two persons talking) or indirect contact (when someone stores knowledge and others can retrieve this knowledge in a remote place or time). In special, chat rooms are becoming important tools for collaboration among people and knowledge exchange. Intelligent software systems may be integrated into chat rooms in order to help people in this collaboration task. For example, systems can identify the theme being discussed and then offer new information or can remember people of existing information sources. This kind of systems is named recommender systems. Furthermore, chat sessions have implicit knowledge about what the participants know and how they are viewing the world. Analyzing chat discussions allows understanding what people are looking for and how people collaborates one with each other. Intelligent software systems can analyze discussions in chats to extract knowledge about the group or about the subject being discussed. Mining tools can analyze chat discussions to understand what is being discussed and help people. For example, a recommender system can analyze textual messages posted in a web chat, identify the subject of the discussion and then look for items stored in a Digital Library to recommend individually to each participant of the discussion. Items can be electronic documents, web pages and bibliographic references stored in a digital library, past discussions and authorities (people with expertise in the subject being discussed). Besides that, mining tools can analyze the whole discussion to map the knowledge exchanged among the chat participants. The benefits of such technology include supporting learning environments, knowledge management efforts within organizations, advertisement and support to decisions.
Mining Email Data
Authors: Bickel Steffen
Published: May 2011

E-mail has become one of the most important communication media for business and private purposes. Large amounts of past e-mail records reside on corporate servers and desktop clients. There is a huge potential for mining this data. E-mail filing and spam filtering are well-established e-mail mining tasks. E-mail filing addresses the assignment of incoming e-mails to predefined categories to support selective reading and organize large e-mail collections. First research on e-mail filing was conducted by Green and Edwards (1996) and Cohen (1996). Pantel and Lin (1998) and Sahami, Dumais, Heckerman, and Horvitz (1998) first published work on spam filtering. Here, the goal is to filter unsolicited messages. Recent research on e-mail mining addresses automatic e-mail answering (Bickel & Scheffer, 2004) and mining social networks from email logs (Tyler, Wilkinson, & Huberman, 2004). In Section Background we will categorize common e-mail mining tasks according to their objective, and give an overview of the research literature. Our Main Thrust Section addresses e-mail mining with the objective of supporting the message creation process. Finally, we discuss Future Trends and conclude.
Mining Group Differences
Authors: Butler Shane M.
Published: May 2011

Finding differences among two or more groups is an important data-mining task. For example, a retailer might want to know what the different is in customer purchasing behaviors during a sale compared to a normal trading day. With this information, the retailer may gain insight into the effects of holding a sale and may factor that into future campaigns. Another possibility would be to investigate what is different about customers who have a loyalty card compared to those who don’t. This could allow the retailer to better understand loyalty cardholders, to increase loyalty revenue, or to attempt to make the loyalty program more appealing to non-cardholders. This article gives an overview of such group mining techniques. First, we discuss two data-mining methods designed specifically for this purpose—Emerging Patterns and Contrast Sets. We will discuss how these two methods relate and how other methods, such as exploratory rule discovery, can also be applied to this task. Exploratory data-mining techniques, such as the techniques used to find group differences, potentially can result in a large number of models being presented to the user. As a result, filter mechanisms can be a useful way to automatically remove models that are unlikely to be of interest to the user. In this article, we will examine a number of such filter mechanisms that can be used to reduce the number of models with which the user is confronted.
Model Assessment with ROC Curves
Authors: Hamel Lutz
Published: May 2011

Classification models and in particular binary classification models are ubiquitous in many branches of science and business. Consider, for example, classification models in bioinformatics that classify catalytic protein structures as being in an active or inactive conformation. As an example from the field of medical informatics we might consider a classification model that, given the parameters of a tumor, will classify it as malignant or benign. Finally, a classification model in a bank might be used to tell the difference between a legal and a fraudulent transaction. Central to constructing, deploying, and using classification models is the question of model performance assessment (Hastie, Tibshirani, & Friedman, 2001). Traditionally this is accomplished by using metrics derived from the confusion matrix or contingency table. However, it has been recognized that (a) a scalar is a poor summary for the performance of a model in particular when deploying non-parametric models such as artificial neural networks or decision trees (Provost, Fawcett, & Kohavi, 1998) and (b) some performance metrics derived from the confusion matrix are sensitive to data anomalies such as class skew (Fawcett & Flach, 2005). Recently it has been observed that Receiver Operating Characteristic (ROC) curves visually convey the same information as the confusion matrix in a much more intuitive and robust fashion (Swets, Dawes, & Monahan, 2000). Here we take a look at model performance metrics derived from the confusion matrix. We highlight their shortcomings and illustrate how ROC curves can be deployed for model assessment in order to provide a much deeper and perhaps more intuitive analysis of the models. We also briefly address the problem of model selection.
Multilingual Text Mining
Authors: Chew Peter A.
Published: May 2011

The principles of text mining are fundamental to technology in everyday use. The world wide web (WWW) has in many senses driven research in text mining, and with the growth of the WWW, applications of text mining (like search engines) have by now become commonplace. In a way that was not true even less than a decade ago, it is taken for granted that the ‘needle in the haystack’ can quickly be found among large volumes of text. In most cases, however, users still expect search engines to return results in the same language as that of the query, perhaps the language best understood by the user, or the language in which text is most likely to be available. The distribution of languages on the WWW does not match the distribution of languages spoken in general by the world’s population. For example, while English is spoken by under 10% of the world’s population (Gordon 2005), it is still predominant on the WWW, accounting for perhaps two-thirds of documents. There are variety of possible reasons for this disparity, including technological inequities between different parts of the world and the fact that the WWW had its genesis in an English-speaking country. Whatever the cause for the dominance of English, the fact that two-thirds of the WWW is in one language is, in all likelihood, a major reason that the concept of multilingual text mining is still relatively new. Until recently, there simply has not been a significant and widespread need for multilingual text mining. A number of recent developments have begun to change the situation, however. Perhaps these developments can be grouped under the general rubric of ‘globalization’. They include the increasing adoption, use, and popularization of the WWW in non-Englishspeaking societies; the trend towards political integration of diverse linguistic communities (highly evident, for example, in the European Union); and a growing interest in understanding social, technological and political developments in other parts of the world. All these developments contribute to a greater demand for multilingual text processing – essentially, methods for handling, managing, and comparing documents in multiple languages, some of which may not even be known to the end user.
Music Information Retrieval
Authors: Wieczorkowska Alicja A.
Published: May 2011

Music information retrieval (MIR) is a multi-disciplinary research on retrieving information from music, see Fig. 1. This research involves scientists from traditional, music and digital libraries, information science, computer science, law, business, engineering, musicology, cognitive psychology and education (Downie, 2001).
Online Signature Recognition
Authors: Chakravarty Indrani
Published: May 2011

Security is one of the major issues in today’s world and most of us have to deal with some sort of passwords in our daily lives; but, these passwords have some problems of their own. If one picks an easy-to-remember password, then it is most likely that somebody else may guess it. On the other hand, if one chooses too difficult a password, then he or she may have to write it somewhere (to avoid inconveniences due to forgotten passwords) which may again lead to security breaches. To prevent passwords being hacked, users are usually advised to keep changing their passwords frequently and are also asked not to keep them too trivial at the same time. All these inconveniences led to the birth of the biometric field. The verification of handwritten signature, which is a behavioral biometric, can be classified into off-line and online signature verification methods. Online signature verification, in general, gives a higher verification rate than off-line verification methods, because of its use of both static and dynamic features of the problem space in contrast to off-line which uses only the static features. Despite greater accuracy, online signature recognition is not that prevalent in comparison to other biometrics. The primary reasons are: • It cannot be used everywhere, especially where signatures have to be written in ink; e.g. on cheques, only off-line methods will work. • Unlike off-line verification methods, online methods require some extra and special hardware, e.g. electronic tablets, pressure sensitive signature pads, etc. For off-line verification method, on the other hand, we can do the data acquisition with optical scanners. • The hardware for online are expensive and have a fixed and short life cycle. In spite of all these inconveniences, the use online methods is on the rise and in the near future, unless a process requires particularly an off-line method to be used, the former will tend to be more and more popular.
Profit Mining
Authors: Zhou Senqiang
Published: May 2011

A major obstacle in data mining applications is the gap between the statistic-based pattern extraction and the value-based decision-making. “Profit mining” aims to reduce this gap. In profit mining, given a set of past transactions and pre-determined target items, we like to build a model for recommending target items and promotion strategies to new customers, with the goal of maximizing profit. Though this problem is studied in the context of retailing environment, the concept and techniques are applicable to other applications under a general notion of “utility”. In this short article, we review existing techniques and briefly describe the profit mining approach recently proposed by the authors. The reader is referred to (Wang, Zhou & Han, 2002) for the details.
Proximity-Graph-Based Tools for DNA Clustering
Authors: Khoury Imad , Toussaint Godfried , Ciampi Antonio , Antoniano Isadora
Published: May 2011

Clustering is considered the most important aspect of unsupervised learning in data mining. It deals with finding structure in a collection of unlabeled data. One simple way of defining clustering is as follows: the process of organizing data elements into groups, called clusters, whose members are similar to each other in some way. Several algorithms for clustering exist (Gan, Ma, & Wu, 2007); proximity-graph-based ones, which are untraditional from the point of view of statisticians, emanate from the field of computational geometry and are powerful and often elegant (Bhattacharya, Mukherjee, & Toussaint, 2005). A proximity graph is a graph formed from a collection of elements, or points, by connecting with an edge those pairs of points that satisfy a particular neighbor relationship with each other. One key aspect of proximity-graph-based clustering techniques is that they may allow for an easy and clear visualization of data clusters, given their geometric nature. Proximity graphs have been shown to improve typical instance-based learning algorithms such as the k-nearest neighbor classifiers in the typical nonparametric approach to classification (Bhattacharya, Mukherjee, & Toussaint, 2005). Furthermore, the most powerful and robust methods for clustering turn out to be those based on proximity graphs (Koren, North, & Volinsky, 2006). Many examples have been shown where proximity-graph-based methods perform very well when traditional methods fail miserably (Zahn, 1971; Choo, Jiamthapthaksin, Chen, Celepcikay, Giusti, & Eick, 2007).
Soft Subspace Clustering for High-Dimensional Data
Authors: Jing Liping , Ng Michael K. , Huang Joshua Zhexue
Published: May 2011

High dimensional data is a phenomenon in real-world data mining applications. Text data is a typical example. In text mining, a text document is viewed as a vector of terms whose dimension is equal to the total number of unique terms in a data set, which is usually in thousands. High dimensional data occurs in business as well. In retails, for example, to effectively manage supplier relationship, suppliers are often categorized according to their business behaviors (Zhang, Huang, Qian, Xu, & Jing, 2006). The supplier’s behavior data is high dimensional, which contains thousands of attributes to describe the supplier’s behaviors, including product items, ordered amounts, order frequencies, product quality and so forth. One more example is DNA microarray data. Clustering high-dimensional data requires special treatment (Swanson, 1990; Jain, Murty, & Flynn, 1999; Cai, He, & Han, 2005; Kontaki, Papadopoulos & Manolopoulos., 2007), although various methods for clustering are available (Jain & Dubes, 1988). One type of clustering methods for high dimensional data is referred to as subspace clustering, aiming at finding clusters from subspaces instead of the entire data space. In a subspace clustering, each cluster is a set of objects identified by a subset of dimensions and different clusters are represented in different subsets of dimensions. Soft subspace clustering considers that different dimensions make different contributions to the identification of objects in a cluster. It represents the importance of a dimension as a weight that can be treated as the degree of the dimension in contribution to the cluster. Soft subspace clustering can find the cluster memberships of objects and identify the subspace of each cluster in the same clustering process.
Summarization in Pattern Mining
Authors: Al Hasan Mohammad
Published: May 2011

The research on mining interesting patterns from transactions or scientific datasets has matured over the last two decades. At present, numerous algorithms exist to mine patterns of variable complexities, such as set, sequence, tree, graph, etc. Collectively, they are referred as Frequent Pattern Mining (FPM) algorithms. FPM is useful in most of the prominent knowledge discovery tasks, like classification, clustering, outlier detection, etc. They can be further used, in database tasks, like indexing and hashing while storing a large collection of patterns. But, the usage of FPM in real-life knowledge discovery systems is considerably low in comparison to their potential. The prime reason is the lack of interpretability caused from the enormity of the output-set size. For instance, a moderate size graph dataset with merely thousand graphs can produce millions of frequent graph patterns with a reasonable support value. This is expected due to the combinatorial search space of pattern mining. However, classification, clustering, and other similar Knowledge discovery tasks should not use that many patterns as their knowledge nuggets (features), as it would increase the time and memory complexity of the system. Moreover, it can cause a deterioration of the task quality because of the popular “curse of dimensionality” effect. So, in recent years, researchers felt the need to summarize the output set of FPM algorithms, so that the summary-set is small, non-redundant and discriminative. There are different summarization techniques: lossless, profile-based, cluster-based, statistical, etc. In this article, we like to overview the main concept of these summarization techniques, with a comparative discussion of their strength, weakness, applicability and computation cost.
Tabu Search for Variable Selection in Classification
Authors: Casado Yusta Silvia , Pacheco Bonrostro Joaquín
Published: May 2011

Variable selection plays an important role in classification. Before beginning the design of a classification method, when many variables are involved, only those variables that are really required should be selected. There can be many reasons for selecting only a subset of the variables instead of the whole set of candidate variables (Reunanen, 2003): (1) It is cheaper to measure only a reduced set of variables, (2) Prediction accuracy may be improved through the exclusion of redundant and irrelevant variables, (3) The predictor to be built is usually simpler and potentially faster when fewer input variables are used and (4) Knowing which variables are relevant can give insight into the nature of the prediction problem and allows a better understanding of the final classification model. The importance of variables selection before using classification methods is also pointed out in recent works such as Cai et al.(2007) and Rao and Lakshminarayanan (2007). The aim in the classification problem is to classify instances that are characterized by attributes or variables. Based on a set of examples (whose class is known) a set of rules is designed and generalised to classify the set of instances with the greatest precision possible. There are several methodologies for dealing with this problem: Classic Discriminant Analysis, Logistic Regression, Neural Networks, Decision Trees, Instance- Based Learning, etc. Linear Discriminant Analysis and Logistic Regression methods search for linear functions and then use them for classification purposes. They continue to be interesting methodologies. In this work an “ad hoc” new method for variable selection in classification, specifically in discriminant analysis and logistic regression, is analysed. This new method is based on the metaheuristic strategy tabu search and yields better results than the classic methods (stepwise, backward and forward) used by statistical packages such as SPSS or BMDP, as it’s shown below. This method is performed for 2 classes.
Vertical Data Mining on Very Large Data Sets
Authors: Perrizo William , Ding Qiang , Ding Qin , Abidin Taufik
Published: May 2011

Due to the rapid growth of the volume of data that are available, it is of importance and challenge to develop scalable methodologies and frameworks that can be used to perform efficient and effective data mining on large data sets. Vertical data mining strategy aims at addressing the scalability issues by organizing data in vertical layouts and conducting logical operations on vertical partitioned data instead of scanning the entire database horizontally in order to perform various data mining tasks.
Visualization of High-Dimensional Data with Polar Coordinates
Authors: Rehm Frank , Klawonn Frank , Kruse Rudolf
Published: May 2011

Many applications in science and business such as signal analysis or costumer segmentation deal with large amounts of data which are usually high dimensional in the feature space. As a part of preprocessing and exploratory data analysis, visualization of the data helps to decide which kind of data mining method probably leads to good results or whether outliers or noisy data need to be treated before (Barnett & Lewis, 1994; Hawkins, 1980). Since the visual assessment of a feature space that has more than three dimensions is not possible, it becomes necessary to find an appropriate visualization scheme for such data sets. Multidimensional scaling (MDS) is a family of methods that seek to present the important structure of the data in a reduced number of dimensions. Due to the approach of distance preservation that is followed by conventional MDS techniques, resource requirements regarding memory space and computation time are fairly high and prevent their application to large data sets. In this work we will present two methods that visualize high-dimensional data on the plane using a new approach. An algorithm will be presented that allows applying our method on larger data sets. We will also present some results on a benchmark data set.
Web Mining Overview
Authors: Mobasher Bamshad
Published: May 2011

In the span of a decade, the World Wide Web has been transformed from a tool for information sharing among researchers into an indispensable part of everyday activities. This transformation has been characterized by an explosion of heterogeneous data and information available electronically, as well as increasingly complex applications driving a variety of systems for content management, e-commerce, e-learning, collaboration, and other Web services. This tremendous growth, in turn, has necessitated the development of more intelligent tools for end users as well as information providers in order to more effectively extract relevant information or to discover actionable knowledge. From its very beginning, the potential of extracting valuable knowledge from the Web has been quite evident. Web mining (i.e. the application of data mining techniques to extract knowledge from Web content, structure, and usage) is the collection of technologies to fulfill this potential. In this article, we will summarize briefly each of the three primary areas of Web mining—Web usage mining, Web content mining, and Web structure mining— and discuss some of the primary applications in each area.