Research and Trends in Data Mining Technologies and Applications

Research and Trends in Data Mining Technologies and Applications

Copyright: © 2007 |Pages: 340

ISBN13: 9781599042718|ISBN10: 1599042711|EISBN13: 9781599042732|ISBN13 Softcover: 9781599042725

DOI: 10.4018/978-1-59904-271-8

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Taniar, David, editor. Research and Trends in Data Mining Technologies and Applications. IGI Global, 2007. https://doi.org/10.4018/978-1-59904-271-8

APA

Taniar, D. (Ed.). (2007). Research and Trends in Data Mining Technologies and Applications. IGI Global. https://doi.org/10.4018/978-1-59904-271-8

Chicago

Taniar, David, ed. Research and Trends in Data Mining Technologies and Applications. Hershey, PA: IGI Global, 2007. https://doi.org/10.4018/978-1-59904-271-8

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Activities in data warehousing and mining are constantly emerging. Data mining methods, algorithms, online analytical processes, data mart and practical issues consistently evolve, providing a challenge for professionals in the field.

Research and Trends in Data Mining Technologies and Applications focuses on the integration between the fields of data warehousing and data mining, with emphasis on the applicability to real-world problems. This book provides an international perspective, highlighting solutions to some of researchers' toughest challenges. Developments in the knowledge discovery process, data models, structures, and design serve as answers and solutions to these emerging challenges.

Table of Contents

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Front Materials

Title Page

Copyright Page

Preface

Chapters

Data Warehousing and Mining

Chapter 1

Combining Data Warehousing and Data Mining Techniques for Web Log Analysis (pages 1-28)

Torben Pedersen, Jesper Thorhauge, Søren Jespersen

Enormous amounts of information about Web site user behavior are collected in Web server logs. However, this information is only useful if it can be queried and analyzed to provide high-level knowledge about user navigation patterns...

Chapter 2

Computing Dense Cubes Embedded in Sparse Data (pages 29-52)

Lixin Fu

In high-dimensional data sets, both the number of dimensions and the cardinalities of the dimensions are large and data is often very sparse, that is, most cubes are empty. For such large data sets, it is a well-known challenging...

Chapter 3

Exploring Similarities Across High-Dimensional Datasets (pages 53-84)

Karlton Sequeira, Mohammed J. Zaki

Very often, related data may be collected by a number of sources, which may be unable to share their entire datasets for reasons like confidentiality agreements, dataset size, and so forth. However, these sources may be willing to...

Patterns

Chapter 4

Pattern Comparison in Data Mining: A Survey (pages 86-120)

Irene Ntoutsi, Nikos Pelekis, Yannis Theodoridis

Many patterns are available nowadays due to the widespread use of knowledge discovery in databases (KDD), as a result of the overwhelming amount of data. This “flood” of patterns imposes new challenges regarding their management....

Chapter 5

Mining Frequent Patterns Using Self-Organizing Map (pages 121-142)

Fedja Hadzic, Tharam Dillon, Henry Tan, Ling. Feng, Elizabeth Chang

Association rule mining is one of the most popular pattern discovery methods used in data mining. Frequent pattern extraction is an essential step in association rule mining. Most of the proposed algorithms for extracting frequent...

Chapter 6

An Efficient Compression Technique for Vertical Mining Methods (pages 143-173)

Mafruz Ashrafi, David Taniar, Kate Smith

Association rule mining is one of the most widely used data mining techniques. To achieve a better performance, many efficient algorithms have been proposed. Despite these efforts, many of these algorithms require a large amount of...

Data Mining in Bioinformatics

Chapter 7

A Tutorial on Hierarchical Classification with Applications in Bioinformatics (pages 175-208)

Alex Freitas, André Carvalho

In machine learning and data mining, most of the works in classification problems deal with flat classification, where each instance is classified in one of a set of possible classes and there is no hierarchical relationship between...

Chapter 8

Topological Analysis and Sub-Network Mining of Protein-Protein Interactions (pages 209-240)

Daniel Wu, Xiaohua Hu

In this chapter, we report a comprehensive evaluation of the topological structure of protein-protein interaction (PPI) networks, by mining and analyzing graphs constructed from the popular data sets publicly available to the...

Data Mining Techniques

Chapter 9

Introduction to Data Mining Techniques via Multiple Criteria Optimization Approaches and Applications (pages 242-275)

Yong Shi, Yi Peng, Gang Kou, Zhengxin Chen

This chapter provides an overview of a series of multiple criteria optimization-based data mining methods, which utilize multiple criteria programming (MCP) to solve data mining problems, and outlines some research challenges and...

Chapter 10

Linguistic Rule Extraction from Support Vector Machine Classifiers (pages 276-290)

Xiuju Fu, Lipo Wang, GihGuang Hung, Liping Goh

Classification decisions from linguistic rules are more desirable compared to complex mathematical formulas from support vector machine (SVM) classifiers due to the explicit explanation capability of linguistic rules. Linguistic rule...

Chapter 11

Graph-Based Data Mining (pages 291-307)

Wenyuan Li, Wee-Keong Ng, Kok-Leong Ong

With the most expressive representation that is able to characterize the complex data, graph mining is an emerging and promising domain in data mining. Meanwhile, the graph has been well studied in a long history with many...

Chapter 12

Facilitating and Improving the Use of Web Services with Data Mining (pages 308-326)

Richi Nayak

Web services have recently received much attention in businesses. However, a number of challenges such as lack of experience in estimating the costs, lack of service innovation and monitoring, and lack of methods for locating...

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