Mining Frequent Patterns Using Self-Organizing Map

Mining Frequent Patterns Using Self-Organizing Map

Fedja Hadzic (University of Technology Sydney, Australia), Tharam Dillon (University of Technology Sydney, Australia), Henry Tan (University of Technology Sydney, Australia), Ling. Feng (University of Twente, The Netherlands) and Elizabeth Chang (Curtin University of Technology, Australia)
Copyright: © 2007 |Pages: 22
DOI: 10.4018/978-1-59904-271-8.ch005
OnDemand PDF Download:
$37.50

Abstract

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 patterns are based on the downward closure lemma concept utilizing the support and confidence framework. In this chapter we investigate an alternative method for mining frequent patterns in a transactional database. Self-Organizing Map (SOM) is an unsupervised neural network that effectively creates spatially organized internal representations of the features and abstractions detected in the input space. It is one of the most popular clustering techniques, and it reveals existing similarities in the input space by performing a topology-preserving mapping. These promising properties indicate that such a clustering technique can be used to detect frequent patterns in a top-down manner as opposed to the traditional approach that employs a bottom-up lattice search. Issues that are frequently raised when using clustering technique for the purpose of finding association rules are: (i) the completeness of association rule set, (ii) the support level for the rules generated, and (iii) the confidence level for the rules generated. We present some case studies analyzing the relationships between the SOM approach and the traditional association rule framework, and propose a way to constrain the clustering technique so that the traditional support constraint can be approximated. Throughout our experiments, we have demonstrated how a clustering approach can be used for discovering frequent patterns.

Complete Chapter List

Search this Book:
Reset
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
$37.50
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
$37.50
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
$37.50
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
$37.50
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
$37.50
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
$37.50
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
$37.50
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
$37.50
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
$37.50
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
$37.50
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
$37.50
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
$37.50
About the Authors