Graph-Based Data Mining

Graph-Based Data Mining

Wenyuan Li (Nanyang Technological University, Singapore), Wee-Keong Ng (Nanyang Technological University, Singapore) and Kok-Leong Ong (Deakin University, Australia)
Copyright: © 2007 |Pages: 17
DOI: 10.4018/978-1-59904-271-8.ch011
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Abstract

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 theoretical results from various foundational fields, such as mathematics, physics, and artificial intelligence. In this chapter, we systematically reviewed theories and techniques newly studied and proposed in these areas. Moreover, we focused on those approaches that are potentially valuable to graph-based data mining. These approaches provide the different perspectives and motivations for this new domain. To illustrate how the method from the other area contributes to graph-based data mining, we did a case study on a classic graph problem that can be widely applied in many application areas. Our results showed that the methods from foundational areas may contribute to graph-based data mining.

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Table of Contents
Preface
David Taniar
Chapter 1
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... Sample PDF
Combining Data Warehousing and Data Mining Techniques for Web Log Analysis
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Chapter 2
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... Sample PDF
Computing Dense Cubes Embedded in Sparse Data
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Chapter 3
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... Sample PDF
Exploring Similarities Across High-Dimensional Datasets
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Chapter 4
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... Sample PDF
Pattern Comparison in Data Mining: A Survey
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Chapter 5
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... Sample PDF
Mining Frequent Patterns Using Self-Organizing Map
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Chapter 6
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... Sample PDF
An Efficient Compression Technique for Vertical Mining Methods
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Chapter 7
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... Sample PDF
A Tutorial on Hierarchical Classification with Applications in Bioinformatics
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Chapter 8
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... Sample PDF
Topological Analysis and Sub-Network Mining of Protein-Protein Interactions
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Chapter 9
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... Sample PDF
Introduction to Data Mining Techniques via Multiple Criteria Optimization Approaches and Applications
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Chapter 10
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)... Sample PDF
Linguistic Rule Extraction from Support Vector Machine Classifiers
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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... Sample PDF
Graph-Based Data Mining
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Chapter 12
Richi Nayak
Web services have recently received much attention in businesses. However, a number of challenges such as lack of experience in estimating the... Sample PDF
Facilitating and Improving the Use of Web Services with Data Mining
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