bluuebunny/crossref_metadata_2025 huggingface dataset:
Classification of Graph Structures
Authors: Dominik Andrzej
Published: May 2011

Classification is a classical and fundamental data mining (machine learning) task in which individual items (objects) are divided into groups (classes) based on their features (attributes). Classification problems have been deeply researched as they have a large variety of applications. They appear in different fields of science and industry and may be solved using different algorithms and techniques: e.g. neural networks, rough sets, fuzzy sets, decision trees, etc. These methods operate on various data representations. The most popular one is information system/decision table (e.g. Dominik, & Walczak, 2006) denoted by a table where rows represent objects, columns represent attributes and every cell holds a value of the given attribute for a particular object. Sometimes it is either very difficult and/or impractical to model a real life object (e.g. road map) or phenomenon (e.g. protein interactions) by a row in decision table (vector of features). In such a cases more complex data representations are required e.g. graphs, networks. A graph is basically a set of nodes (vertices) connected by either directed or undirected edges (links). Graphs are used to model and solve a wide variety of problems including classification. Recently a huge interest in the area of graph mining can be observed (e.g. Cook, & Holder, 2006). This field of science concentrates on investigating and discovering relevant information from data represented by graphs. In this chapter, we present basic concepts, problems and methods connected with graph structures classification. We evaluate performance of the most popular and effective classifiers on two kinds of classification problems from different fields of science: computational chemistry, chemical informatics (chemical compounds classification) and information science (web documents classification).
Clustering of Time Series Data
Authors: Denton Anne
Published: May 2011

Time series data is of interest to most science and engineering disciplines and analysis techniques have been developed for hundreds of years. There have, however, in recent years been new developments in data mining techniques, such as frequent pattern mining, that take a different perspective of data. Traditional techniques were not meant for such pattern-oriented approaches. There is, as a result, a significant need for research that extends traditional time-series analysis, in particular clustering, to the requirements of the new data mining algorithms.
On Clustering Techniques
Authors: Ma Sheng , Li Tao
Published: May 2011

Clustering data into sensible groupings, as a fundamental and effective tool for efficient data organization, summarization, understanding and learning, has been the subject of active research in several fields such as statistics (Jain & Dubes, 1988; Hartigan, 1975), machine learning (Dempster, Laird & Rubin, 1977), Information theory (Linde, Buzo & Gray, 1980), databases (Guha, Rastogi & Shim, 1998; Zhang, Ramakrishnan & Livny, 1996) and Bioinformatics (Cheng & Church, 2000) from various perspectives and with various approaches and focuses. From application perspective, clustering techniques have been employed in a wide variety of applications such as customer segregation, hierarchal document organization, image segmentation, microarray data analysis and psychology experiments. Intuitively, the clustering problem can be described as follows: Let W be a set of n entities, finding a partition of W into groups such that the entities within each group are similar to each other while entities belonging to different groups are dissimilar. The entities are usually described by a set of measurements (attributes). Clustering does not use category information that labels the objects with prior identifiers. The absence of label information distinguishes cluster analysis from classification and indicates that the goals of clustering is just finding a hidden structure or compact representation of data instead of discriminating future data into categories.
Comparing Four-Selected Data Mining Software
Authors: Segall Richard S.
Published: May 2011

This chapter discusses four-selected software for data mining that are not available as free open-source software. The four-selected software for data mining are SAS® Enterprise MinerTM, Megaputer PolyAnalyst® 5.0, NeuralWare Predict® and BioDiscovery GeneSight ®, each of which was provided by partnerships with our university. These software are described and compared by their existing features, characteristics, and algorithms and also applied to a large database of forest cover types with 63,377 rows and 54 attributes. Background on related literature and software are also presented. Screen shots of each of the four-selected software are presented, as are future directions and conclusions.
Context-Driven Decision Mining
Authors: mirnov Alexander
Published: May 2011

Decisions in the modern world are often made in rapidly changing, sometimes unexpected, situations. Such situations require availability of systems / tools allowing fast and clear description of situation, generation of new and reuse of previously made effective solutions for situation reformation, selection of a right decision maker and supplying him/her with necessary data. Such tools include components for actual situation description, user modeling, finding appropriate methods for problem solving, integration of data from heterogeneous sources, finding / generation of insufficient data, removing uncertainties, estimating solutions, etc. During decision making process a large amount of auxiliary raw data are accumulated in repositories. Methods of data mining are used in such systems for different purposes: finding associative rules between decisions and factors affecting them, user clustering using decision trees and neural networks, recognition of common users’ features / interests and others (Chiang et al., 2006; Li, 2005; Thomassey and Fiordaliso, 2006). Validation of the obtained results can be performed using simulation software modules.
Data Driven vs. Metric Driven Data Warehouse Design
Authors: Artz John M.
Published: May 2011

Although data warehousing theory and technology have been around for well over a decade, they may well be the next hot technologies. How can it be that a technology sleeps for so long and then begins to move rapidly to the foreground? This question can have several answers. Perhaps the technology had not yet caught up to the theory or that computer technology 10 years ago did not have the capacity to delivery what the theory promised. Perhaps the ideas and the products were just ahead of their time. All these answers are true to some extent. But the real answer, I believe, is that data warehousing is in the process of undergoing a radical theoretical and paradigmatic shift, and that shift will reposition data warehousing to meet future demands.
Data Streams
Authors: Gama João , Rodrigues Pedro Pereira
Published: May 2011

Nowadays, data bases are required to store massive amounts of data that are continuously inserted, and queried. Organizations use decision support systems to identify potential useful patterns in data. Data analysis is complex, interactive, and exploratory over very large volumes of historic data, eventually stored in distributed environments. What distinguishes current data sources from earlier ones are the continuous flow of data and the automatic data feeds. We do not just have people who are entering information into a computer. Instead, we have computers entering data into each other (Muthukrishnan, 2005). Some illustrative examples of the magnitude of today data include: 3 billion telephone calls per day, 4 Giga Bytes of data from radio telescopes every night, 30 billion emails per day, 1 billion SMS, 5 Giga Bytes of Satellite Data per day, 70 billion IP Network Traffic per day. In these applications, data is modelled best not as persistent tables but rather as transient data streams. In some applications it is not feasible to load the arriving data into a traditional Data Base Management Systems (DBMS), and traditional DBMS are not designed to directly support the continuous queries required in these applications (Alon et al., 1996; Babcock et al. 2002; Cormode & Muthukrishnan, 2003). These sources of data are called Data Streams. Computers play a much more active role in the current trends in decision support and data analysis. Data mining algorithms search for hypothesis, evaluate and suggest patterns. The pattern discovery process requires online ad-hoc queries, not previously defined, that are successively refined. Due to the exploratory nature of these queries, an exact answer may be not required: a user may prefer a fast but approximate answer to a exact but slow answer. Processing queries in streams require radically different type of algorithms. Range queries and selectivity estimation (the proportion of tuples that satisfy a query) are two illustrative examples where fast but approximate answers are more useful than slow and exact ones. Approximate answers are obtained from small data structures (synopsis) attached to data base that summarize information and can be updated incrementally
Database Queries, Data Mining, and OLAP
Authors: Hamel Lutz
Published: May 2011

Modern, commercially available relational database systems now routinely include a cadre of data retrieval and analysis tools. Here we shed some light on the interrelationships between the most common tools and components included in today’s database systems: query language engines, data mining components, and on-line analytical processing (OLAP) tools. We do so by pair-wise juxtaposition which will underscore their differences and highlight their complementary value.
Discovery Informatics from Data to Knowledge
Authors: Agresti William W.
Published: May 2011

It is routine to hear and read about the information explosion, how we are all overwhelmed with data and information. Is it progress when our search tools report that our query resulted in 300,000 hits? Or, are we still left to wonder where is the information that we really wanted? How far down the list must we go to find it? Discovery informatics is a distinctly 21st century emerging methodology that brings together several threads of research and practice aimed at making sense out of massive data sources. It is defined as “the study and practice of employing the full spectrum of computing and analytical science and technology to the singular pursuit of discovering new information by identifying and validating patterns in data” (Agresti, 2003).
Distributed Data Aggregation Technology for Real-Time DDoS Attacks Detection
Authors: Chen Yu , Ku Wei-Shinn
Published: May 2011

The information technology has revolutionized almost every facet of our lives. Government, commercial, and educational organizations depend on computers and Internet to such an extent that day-to-day operations are significantly hindered when the networks are “down” (Gordon, Loeb, Lucyshyn & Richardson, 2005). The prosperity of the Internet also attracted abusers and attackers motivated for personal, financial, or even political reasons. What attackers aim at currently is beyond obtaining unauthorized network accesses or stealing private information, there have been attacks on Internet infrastructures (Chakrabarti & Manimaran, 2002; Moore, Voelker & Savage, 2001; Naoumov & Ross, 2006). Distributed Denial of Services (DDoS) attacks is one of such attacks that can lead to enormous destruction, as different infrastructure components of the Internet have implicit trust relationship with each other (Mirkovic & Reiher, 2004; Specht & Lee, 2004). The DDoS attacker often exploits the huge resource asymmetry between the Internet and the victim systems (Chen, Hwang & Ku, 2007; Douligeris & Mitrokosta, 2003). A comprehensive solution to DDoS attacks requires covering global effects over a wide area of autonomous system (AS) domains on the Internet (Mirkovic & Reiher, 2005). Timely detection of the ongoing attacks is the prerequisite of any effective defense scheme (Carl, Kesidis, Brooks & Rai, 2006). It is highly desirable to detect DDoS attacks at very early stage, instead of waiting for the flood to become widespread. It is mandatory for the detection systems to collect real time traffic data from widely deployed traffic monitors and construct the spatiotemporal pattern of anomaly propagation inside the network. This chapter will introduce a novel distributed real time data aggregation technique named Change Aggregation Tree (CAT). The CAT system adopts a hierarchical architecture to simplify the alert correlation and global detection procedures. At intra-domain level, each individual router, which plays the role of traffic monitor, periodically report the local traffic status to the CAT server in the AS. At the inter-domain layer, CAT servers share local detected anomaly patterns with peers located in other ASes, where the potential attack victim is located.
Ensemble Data Mining Methods
Authors: Oza Nikunj C.
Published: May 2011

Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: each member of the committee should be as competent as possible, but the members should be complementary to one another. If the members are not complementary, that is, if they always agree, then the committee is unnecessary—any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.
Ensemble Learning for Regression
Authors: Rooney Niall
Published: May 2011

The concept of ensemble learning has its origins in research from the late 1980s/early 1990s into combining a number of artificial neural networks (ANNs) models for regression tasks. Ensemble learning is now a widely deployed and researched topic within the area of machine learning and data mining. Ensemble learning, as a general definition, refers to the concept of being able to apply more than one learning model to a particular machine learning problem using some method of integration. The desired goal of course is that the ensemble as a unit will outperform any of its individual members for the given learning task. Ensemble learning has been extended to cover other learning tasks such as classification (refer to Kuncheva, 2004 for a detailed overview of this area), online learning (Fern & Givan, 2003) and clustering (Strehl & Ghosh, 2003). The focus of this article is to review ensemble learning with respect to regression, where by regression, we refer to the supervised learning task of creating a model that relates a continuous output variable to a vector of input variables.
The Evolution of SDI Geospatial Data Clearinghouses
Authors: Kelly Maurie Caitlin
Published: May 2011

Geospatial data and the technologies that drive them have altered the landscape of our understanding of the world around us. The data, software and services related to geospatial information have given us the opportunity to visualize existing phenomena, to understand connections, and to address problems from environmental management to emergency response. From the everpresent Google Earth images we are shown in our televised weather reports to the 3D flyovers of war zones on the news, geospatial information is everywhere. In the decade or so since U.S. President William Clinton set the stage by announcing the establishment of the National Spatial Data Infrastructure (NSDI), the concept of the geospatial data clearinghouse has shifted dramatically to fulfill the increasing need to streamline government processes, increase collaboration, and to meet the demands of data users and data developers (Clinton, 1994). The announcement of the NSDI gave birth to a Global Spatial Data Infrastructure (GSDI) movement that would be supported by a network of SDIs or geospatial data clearinghouses from local, state, and national levels. From this point on, the evolution of the geospatial data clearinghouse has been rapid and punctuated with challenges to both the developer and the user. From the earliest incarnations of these now pervasive resources as simple FTP data transfer sites to the latest developments in Internet Map Services and real time data services, geospatial data clearinghouses have provided the backbone for the exponential growth of Geographic Information Systems (GIS). In this section, the authors will examine the background of the geospatial data clearinghouse movement, address the basic phases of clearinghouse development, and review the trends that have taken the world’s clearinghouses from FTP to Internet Map Services and beyond.
Evolutionary Computation and Genetic Algorithms
Authors: Hsu William H.
Published: May 2011

A genetic algorithm (GA) is a method used to find approximate solutions to difficult search, optimization, and machine learning problems (Goldberg, 1989) by applying principles of evolutionary biology to computer science. Genetic algorithms use biologically-derived techniques such as inheritance, mutation, natural selection, and recombination. They are a particular class of evolutionary algorithms. Genetic algorithms are typically implemented as a computer simulation in which a population of abstract representations (called chromosomes) of candidate solutions (called individuals) to an optimization problem evolves toward better solutions. Traditionally, solutions are represented in binary as strings of 0s and 1s, but different encodings are also possible. The evolution starts from a population of completely random individuals and happens in generations. In each generation, multiple individuals are stochastically selected from the current population, modified (mutated or recombined) to form a new population, which becomes current in the next iteration of the algorithm.
Feature Selection
Authors: François Damien
Published: May 2011

In many applications, like function approximation, pattern recognition, time series prediction, and data mining, one has to build a model relating some features describing the data to some response value. Often, the features that are relevant for building the model are not known in advance. Feature selection methods allow removing irrelevant and/or redundant features to only keep the feature subset that are most useful to build a prediction model. The model is simpler and easier to interpret, reducing the risks of overfitting, non-convergence, etc. By contrast with other dimensionality reduction techniques such as principal component analysis or more recent nonlinear projection techniques (Lee & Verleysen 2007), which build a new, smaller set of features, the features that are selected by feature selection methods preserve their initial meaning, potentially bringing extra information about the process being modeled (Guyon 2006). Recently, the advent of high-dimensional data has raised new challenges for feature selection methods, both from the algorithmic point of view and the conceptual point of view (Liu & Motoda 2007). The problem of feature selection is exponential in nature, and many approximate algorithms are cubic with respect to the initial number of features, which may be intractable when the dimensionality of the data is large. Furthermore, high-dimensional data are often highly redundant, and two distinct subsets of features may have very similar predictive power, which can make it difficult to identify the best subset.
Financial Time Series Data Mining
Authors: Bose Indranil
Published: May 2011

Movement of stocks in the financial market is a typical example of financial time series data. It is generally believed that past performance of a stock can indicate its future trend and so stock trend analysis is a popular activity in the financial community. In this chapter, we will explore the unique characteristics of financial time series data mining. Financial time series analysis came into being recently. Though the world’s first stock exchange was established in the 18th century, stock trend analysis began only in the late 20th century. According to Tay et al. (2003) analysis of financial time series has been formally addressed only since 1980s. It is believed that financial time series data can speak for itself. By analyzing the data, one can understand the volatility, seasonal effects, liquidity, and price response and hence predict the movement of a stock. For example, the continuous downward movement of the S&P index during a short period of time allows investors to anticipate that majority of stocks will go down in immediate future. On the other hand, a sharp increase in interest rate makes investors speculate that a decrease in overall bond price will occur. Such conclusions can only be drawn after a detailed analysis of the historic stock data. There are many charts and figures related to stock index movements, change of exchange rates, and variations of bond prices, which can be encountered everyday. An example of such a financial time series data is shown in Figure 1. It is generally believed that through data analysis, analysts can exploit the temporal dependencies both in the deterministic (regression) and the stochastic (error) components of a model and can come up with better prediction models for future stock prices (Congdon, 2003).
Fuzzy Methods in Data Mining
Authors: Hüllermeier Eyke
Published: May 2011

Tools and techniques that have been developed during the last 40 years in the field of fuzzy set theory (FST) have been applied quite successfully in a variety of application areas. A prominent example of the practical usefulness of corresponding techniques is fuzzy control, where the idea is to represent the input-output behaviour of a controller (of a technical system) in terms of fuzzy rules. A concrete control function is derived from such rules by means of suitable inference techniques. While aspects of knowledge representation and reasoning have dominated research in FST for a long time, problems of automated learning and knowledge acquisition have more and more come to the fore in recent years. There are several reasons for this development, notably the following: Firstly, there has been an internal shift within fuzzy systems research from “modelling” to “learning”, which can be attributed to the awareness that the well-known “knowledge acquisition bottleneck” seems to remain one of the key problems in the design of intelligent and knowledge-based systems. Secondly, this trend has been further amplified by the great interest that the fields of knowledge discovery in databases (KDD) and its core methodical component, data mining, have attracted in recent years. It is hence hardly surprising that data mining has received a great deal of attention in the FST community in recent years (Hüllermeier, 2005). The aim of this chapter is to give an idea of the usefulness of FST for data mining. To this end, we shall briefly highlight, in the next but one section, some potential advantages of fuzzy approaches. In preparation, the next section briefly recalls some basic ideas and concepts from FST. The style of presentation is purely non-technical throughout; for technical details we shall give pointers to the literature.
A General Model for Data Warehouses
Authors: Schneider Michel
Published: May 2011

Basically, the schema of a data warehouse lies on two kinds of elements: facts and dimensions. Facts are used to memorize measures about situations or events. Dimensions are used to analyse these measures, particularly through aggregation operations (counting, summation, average, etc.). To fix the ideas let us consider the analysis of the sales in a shop according to the product type and to the month in the year. Each sale of a product is a fact. One can characterize it by a quantity. One can calculate an aggregation function on the quantities of several facts. For example, one can make the sum of quantities sold for the product type “mineral water” during January in 2001, 2002 and 2003. Product type is a criterion of the dimension Product. Month and Year are criteria of the dimension Time. A quantity is so connected both with a type of product and with a month of one year. This type of connection concerns the organization of facts with regard to dimensions. On the other hand a month is connected to one year. This type of connection concerns the organization of criteria within a dimension. The possibilities of fact analysis depend on these two forms of connection and on the schema of the warehouse. This schema is chosen by the designer in accordance with the users needs. Determining the schema of a data warehouse cannot be achieved without adequate modelling of dimensions and facts. In this article we present a general model for dimensions and facts and their relationships. This model will facilitate greatly the choice of the schema and its manipulation by the users.
Genetic Programming
Authors: Hsu William H.
Published: May 2011

Genetic programming (GP) is a sub-area of evolutionary computation first explored by John Koza (1992) and independently developed by Nichael Lynn Cramer (1985). It is a method for producing computer programs through adaptation according to a user-defined fitness criterion, or objective function. Like genetic algorithms, GP uses a representation related to some computational model, but in GP, fitness is tied to task performance by specific program semantics. Instead of strings or permutations, genetic programs are most commonly represented as variable-sized expression trees in imperative or functional programming languages, as grammars (O’Neill & Ryan, 2001), or as circuits (Koza et al., 1999). GP uses patterns from biological evolution to evolve programs: • Crossover: Exchange of genetic material such as program subtrees or grammatical rules • Selection: The application of the fitness criterion to choose which individuals from a population will go on to reproduce • Replication: The propagation of individuals from one generation to the next • Mutation: The structural modification of individuals To work effectively, GP requires an appropriate set of program operators, variables, and constants. Fitness in GP is typically evaluated over fitness cases. In data mining, this usually means training and validation data, but cases can also be generated dynamically using a simulator or directly sampled from a real-world problem solving environment. GP uses evaluation over these cases to measure performance over the required task, according to the given fitness criterion.
Global Induction of Decision Trees
Authors: Kretowski Marek , Grzes Marek
Published: May 2011

Decision trees are, besides decision rules, one of the most popular forms of knowledge representation in Knowledge Discovery in Databases process (Fayyad, Piatetsky-Shapiro, Smyth & Uthurusamy, 1996) and implementations of the classical decision tree induction algorithms are included in the majority of data mining systems. A hierarchical structure of a tree-based classifier, where appropriate tests from consecutive nodes are subsequently applied, closely resembles a human way of decision making. This makes decision trees natural and easy to understand even for an inexperienced analyst. The popularity of the decision tree approach can also be explained by their ease of application, fast classification and what may be the most important, their effectiveness. Two main types of decision trees can be distinguished by the type of tests in non-terminal nodes: univariate and multivariate decision trees. In the first group, a single attribute is used in each test. For a continuousvalued feature usually an inequality test with binary outcomes is applied and for a nominal attribute mutually exclusive groups of attribute values are associated with outcomes. As a good representative of univariate inducers, the well-known C4.5 system developed by Quinlan (1993) should be mentioned. In univariate trees a split is equivalent to partitioning the feature space with an axis-parallel hyper-plane. If decision boundaries of a particular dataset are not axis-parallel, using such tests may lead to an overcomplicated classifier. This situation is known as the “staircase effect”. The problem can be mitigated by applying more sophisticated multivariate tests, where more than one feature can be taken into account. The most common form of such tests is an oblique split, which is based on a linear combination of features (hyper-plane). The decision tree which applies only oblique tests is often called oblique or linear, whereas heterogeneous trees with univariate, linear and other multivariate (e.g., instance-based) tests can be called mixed decision trees (Llora & Wilson, 2004). It should be emphasized that computational complexity of the multivariate induction is generally significantly higher than the univariate induction. CART (Breiman, Friedman, Olshen & Stone, 1984) and OC1 (Murthy, Kasif & Salzberg, 1994) are well known examples of multivariate systems.
On Interacting Features in Subset Selection
Authors: Zhao Zheng
Published: May 2011

The high dimensionality of data poses a challenge to learning tasks such as classification. In the presence of many irrelevant features, classification algorithms tend to overfit training data (Guyon & Elisseeff, 2003). Many features can be removed without performance deterioration, and feature selection is one effective means to remove irrelevant features (Liu & Yu, 2005). Feature selection, also known as variable selection, feature reduction, attribute selection or variable subset selection, is the technique of selecting a subset of relevant features for building robust learning models. Usually a feature is relevant due to two reasons: (1) it is strongly correlated with the target concept; or (2) it forms a feature subset with other features and the subset is strongly correlated with the target concept. Optimal feature selection requires an exponentially large search space (O(2n), where n is the number of features) (Almual-lim & Dietterich, 1994). Researchers often resort to various approximations to determine relevant features, and in many existing feature selection algorithms, feature relevance is determined by correlation between individual features and the class (Hall, 2000; Yu & Liu, 2003). However, a single feature can be considered irrelevant based on its correlation with the class; but when combined with other features, it can become very relevant. Unintentional removal of these features can result in the loss of useful information and thus may cause poor classification performance, which is studied as attribute interaction in (Jakulin & Bratko, 2003). Therefore, it is desirable to consider the effect of feature interaction in feature selection.
On Interactive Data Mining
Authors: Zhao Yan
Published: May 2011

Exploring and extracting knowledge from data is one of the fundamental problems in science. Data mining consists of important tasks, such as description, prediction and explanation of data, and applies computer technologies to nontrivial calculations. Computer systems can maintain precise operations under a heavy information load, and also can maintain steady performance. Without the aid of computer systems, it is very difficult for people to be aware of, to extract, to search and to retrieve knowledge in large and separate datasets, let alone interpreting and evaluating data and information that are constantly changing, and then making recommendations or predictions based on inconsistent and/or incomplete data. On the other hand, the implementations and applications of computer systems reflect the requests of human users, and are affected by human judgement, preference and evaluation. Computer systems rely on human users to set goals, to select alternatives if an original approach fails, to participate in unanticipated emergencies and novel situations, and to develop innovations in order to preserve safety, avoid expensive failure, or increase product quality (Elm, et al., 2004; Hancock & Scallen, 1996; Shneiderman, 1998). Users possess varied skills, intelligence, cognitive styles, and levels of tolerance of frustration. They come to a problem with diverse preferences, requirements and background knowledge. Given a set of data, users will see it from different angles, in different aspects, and with different views. Considering these differences, a universally applicable theory or method to serve the needs of all users does not exist. This motivates and justifies the co-existence of numerous theories and methods of data mining systems, as well as the exploration of new theories and methods. According to the above observations, we believe that interactive systems are required for data mining tasks. Generally, interactive data mining is an integration of human factors and artificial intelligence (Maanen, Lindenberg and Neerincx, 2005); an interactive system is an integration of a human user and a computer machine, communicating and exchanging information and knowledge. Through interaction and communication, computers and users can share the tasks involved in order to achieve a good balance of automation and human control. Computers are used to retrieve and keep track of large volumes of data, and to carry out complex mathematical or logical operations. Users can then avoid routine, tedious and error-prone tasks, concentrate on critical decision making and planning, and cope with unexpected situations (Elm, et al., 2004; Shneiderman, 1998). Moreover, interactive data mining can encourage users’ learning, improve insight and understanding of the problem to be solved, and stimulate users to explore creative possibilities. Users’ feedback can be used to improve the system. The interaction is mutually beneficial, and imposes new coordination demands on both sides.
The Issue of Missing Values in Data Mining
Authors: Beynon Malcolm J.
Published: May 2011

The essence of data mining is to investigate for pertinent information that may exist in data (often large data sets). The immeasurably large amount of data present in the world, due to the increasing capacity of storage media, manifests the issue of the presence of missing values (Olinsky et al., 2003; Brown and Kros, 2003). The presented encyclopaedia article considers the general issue of the presence of missing values when data mining, and demonstrates the effect of when managing their presence is or is not undertaken, through the utilisation of a data mining technique. The issue of missing values was first exposited over forty years ago in Afifi and Elashoff (1966). Since then it is continually the focus of study and explanation (El-Masri and Fox-Wasylyshyn, 2005), covering issues such as the nature of their presence and management (Allison, 2000). With this in mind, the naïve consistent aspect of the missing value debate is the limited general strategies available for their management, the main two being either the simple deletion of cases with missing data or a form of imputation of the missing values in someway (see Elliott and Hawthorne, 2005). Examples of the specific investigation of missing data (and data quality), include in; data warehousing (Ma et al., 2000), and customer relationship management (Berry and Linoff, 2000). An alternative strategy considered is the retention of the missing values, and their subsequent ‘ignorance’ contribution in any data mining undertaken on the associated original incomplete data set. A consequence of this retention is that full interpretability can be placed on the results found from the original incomplete data set. This strategy can be followed when using the nascent CaRBS technique for object classification (Beynon, 2005a, 2005b). CaRBS analyses are presented here to illustrate that data mining can manage the presence of missing values in a much more effective manner than the more inhibitory traditional strategies. An example data set is considered, with a noticeable level of missing values present in the original data set. A critical increase in the number of missing values present in the data set further illustrates the benefit from ‘intelligent’ data mining (in this case using CaRBS).
Learning with Partial Supervision
Authors: Bouchachia Abdelhamid
Published: May 2011

Recently the field of machine learning, pattern recognition, and data mining has witnessed a new research stream that is learning with partial supervision -LPS- (known also as semi-supervised learning). This learning scheme is motivated by the fact that the process of acquiring the labeling information of data could be quite costly and sometimes prone to mislabeling. The general spectrum of learning from data is envisioned in Figure 1. As shown, in many situations, the data is neither perfectly nor completely labeled.

LPS aims at using available labeled samples in order to guide the process of building classification and clustering machineries and help boost their accuracy. Basically, LPS is a combination of two learning paradigms: supervised and unsupervised where the former deals exclusively with labeled data and the latter is concerned with unlabeled data. Hence, the following questions:

Can we improve supervised learning with unlabeled data? 

Can we guide unsupervised learning by incorporating few labeled samples?

Typical LPS applications are medical diagnosis (Bouchachia & Pedrycz, 2006a), facial expression recognition (Cohen et al., 2004), text classification (Nigam et al., 2000), protein classification (Weston et al., 2003), and several natural language processing applications such as word sense disambiguation (Niu et al., 2005), and text chunking (Ando & Zhangz, 2005).

Because LPS is still a young but active research field, it lacks a survey outlining the existing approaches and research trends. In this chapter, we will take a step towards an overview. We will discuss (i) the background of LPS, (iii) the main focus of our LPS research and explain the underlying assumptions behind LPS, and (iv) future directions and challenges of LPS research.
Matrix Decomposition Techniques for Data Privacy
Authors: Zhang Jun , Wang Jie , Xu Shuting
Published: May 2011

Data mining technologies have now been used in commercial, industrial, and governmental businesses, for various purposes, ranging from increasing profitability to enhancing national security. The widespread applications of data mining technologies have raised concerns about trade secrecy of corporations and privacy of innocent people contained in the datasets collected and used for the data mining purpose. It is necessary that data mining technologies designed for knowledge discovery across corporations and for security purpose towards general population have sufficient privacy awareness to protect the corporate trade secrecy and individual private information. Unfortunately, most standard data mining algorithms are not very efficient in terms of privacy protection, as they were originally developed mainly for commercial applications, in which different organizations collect and own their private databases, and mine their private databases for specific commercial purposes. In the cases of inter-corporation and security data mining applications, data mining algorithms may be applied to datasets containing sensitive or private information. Data warehouse owners and government agencies may potentially have access to many databases collected from different sources and may extract any information from these databases. This potentially unlimited access to data and information raises the fear of possible abuse and promotes the call for privacy protection and due process of law. Privacy-preserving data mining techniques have been developed to address these concerns (Fung et al., 2007; Zhang, & Zhang, 2007). The general goal of the privacy-preserving data mining techniques is defined as to hide sensitive individual data values from the outside world or from unauthorized persons, and simultaneously preserve the underlying data patterns and semantics so that a valid and efficient decision model based on the distorted data can be constructed. In the best scenarios, this new decision model should be equivalent to or even better than the model using the original data from the viewpoint of decision accuracy. There are currently at least two broad classes of approaches to achieving this goal. The first class of approaches attempts to distort the original data values so that the data miners (analysts) have no means (or greatly reduced ability) to derive the original values of the data. The second is to modify the data mining algorithms so that they allow data mining operations on distributed datasets without knowing the exact values of the data or without direct accessing the original datasets. This article only discusses the first class of approaches. Interested readers may consult (Clifton et al., 2003) and the references therein for discussions on distributed data mining approaches.
Mining Generalized Web Data for Discovering Usage Patterns
Authors: Tanasa Doru
Published: May 2011

Web Usage Mining (WUM) includes all the Data Mining techniques used to analyze the behavior of a Web site‘s users (Cooley, Mobasher & Srivastava, 1999, Spiliopoulou, Faulstich & Winkler, 1999, Mobasher, Dai, Luo & Nakagawa, 2002). Based mainly on the data stored into the access log files, these methods allow the discovery of frequent behaviors. In particular, the extraction of sequential patterns (Agrawal, & Srikant, 1995) is well suited to the context of Web logs analysis, given the chronological nature of their records. On a Web portal, one could discover for example that “25% of the users navigated on the site in a particular order, by consulting first the homepage then the page with an article about the bird flu, then the Dow Jones index evolution to finally return on the homepage before consulting their personal e-mail as a subscriber”. In theory, this analysis allows us to find frequent behaviors rather easily. However, reality shows that the diversity of the Web pages and behaviors makes this approach delicate. Indeed, it is often necessary to set minimum thresholds of frequency (i.e. minimum support) of about 1% or 2% before revealing these behaviors. Such low supports combined with significant characteristics of access log files (e.g. huge number of records) are generally the cause of failures or limitations for the existent techniques employed in Web usage analysis. A solution for this problem consists in clustering the pages by topic, in the form of a taxonomy for example, in order to obtain a more general behavior. Considering again the previous example, one could have obtained: “70% of the users navigate on the Web site in a particular order, while consulting the home page then a page of news, then a page on financial indexes, then return on the homepage before consulting a service of communication offered by the Web portal”. A page on the financial indexes can relate to the Dow Jones as well as the FTSE 100 or the NIKKEI (and in a similar way: the e-mail or the chat are services of communication, the bird flu belongs to the news section, etc.). Moreover, the fact of grouping these pages under the “financial indexes” term has a direct impact by increasing the support of such behaviors and thus their readability, their relevance and significance. The drawback of using a taxonomy comes from the time and energy necessary to its definition and maintenance. In this chapter, we propose solutions to facilitate (or guide as much as possible) the automatic creation of this taxonomy allowing a WUM process to return more effective and relevant results. These solutions include a prior clustering of the pages depending on the way they are reached by the users. We will show the relevance of our approach in terms of efficiency and effectiveness when extracting the results.
Mining the Internet for Concepts
Authors: Brena Ramon F. , Maguitman Ana
Published: May 2011

The Internet has made available a big number of information services, such as file sharing, electronic mail, online chat, telephony and file transfer. However, services that provide effective access to Web pages, such as Google, are the ones that most contributed to the popularization and success of the World Wide Web and the Internet. Pages published at the World Wide Web belong to many different topic areas, such as music, fishing, travel, etc. Some organizations have tried to organize pages in a predefined classification, and have manually built large directories of topics (e.g. Dmoz or the Yahoo! directory). But given the huge size and the dynamic nature of the Web, keeping track of pages and their topic manually is a daunting task. There is also the problem of agreeing on a standard classification, and this has proved to be a formidable problem, as different individuals and organizations tend to classify things differently. Another option is to rely on automatic tools that mine the Web for “topics” or “concepts” related to online documents. This approach is indeed more scalable than the manual one. However, automatically classifying documents in topics is a major research challenge. This is because the document keywords alone seem to be insufficient to directly convey the meaning of the document to an autonomous system. In some cases, the main difficulty is due to the ambiguity of the terms encountered in the document. Even if the ambiguity problems were solved there is still no guarantee that the vocabulary used to describe the document will match that used by the autonomous system to guide its search. Central to automatic approaches is the notion of “semantic context”, which loosely means the subject or topic where a task like searching is embedded. Of course, we need a way to computationally represent this notion of context, and one possibility is to see context as a collection of interrelated terms in the sense that they appear together in a number of related pages (Ramirez & Brena, 2006). For instance, the word “Java” appears together with “roasted” when talking about coffee, but appears more frequently with “code” when talking about a programming language. Semantic contexts allow performing searches on the Web at the concept level, rather than at the more basic keyword level. In this chapter we present recent advances in automated approaches in web concept mining, emphasizing our own work about mining the Web for semantic contexts.