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Title of Thesis

Mining Frequent Patterns Without Minimum Support Threshold

Author(s)

Abdus Salam

Institute/University/Department Details
Department Of Computer Science, Faculty of Basic And Applied Science / International Islamic University, Islamabad
Session
2011
Subject
Computer Science
Number of Pages
155
Keywords (Extracted from title, table of contents and abstract of thesis)
Patterns, Mechanism, Classical, Threshold, Frequent, Support, Method, Graph, Parameters, Minimum, Framework, Mining, Advantages, Classification, Maximal

Abstract
Finding frequent patterns is the cornerstone of many classical data mining tasks such as associations, correlations, sequences, episodes, classification, and clustering.It is also essential to a wide range of emerging applications, like Web-log mining, click-stream mining, network traffic analysis, stock market analysis, and sensor networks.Majority of existing frequent pattern mining approaches require many input parameters to be set by the users.The most widely-used parameter is the minimum support threshold to extract statistically significant patterns and to prune out the insignificant patterns.The selection of minimum support is somewhat arbitrary and there is no mechanism to ensure that this may not inadvertently remove many of the interesting patterns.Calculating support counts for the candidate itemsets consumes most of the execution time of exiting techniques.A frequent pattern mining method without minimum support threshold avoids costly candidate-generation-and-test techniques completely to give major gains in terms of performance and efficiency.This study presents a novel method to discover maximal frequent itemsets using a single database pass.Initially all 2-itemsets are generated and then association ratio among them is computed.Then an association ratio graph is constructed to facilitate the maximal frequent itemsets generation.Efficient algorithms are described for using this compact graph data structures to discover top-most and top-k maximal frequent itemsets without user specified minimum support threshold.This method employs the breadth-first search approach to construct the all-path-source-todestination tree and then finds the top few maximal frequent itemsets by traversing all source to destination paths.Results are presented demonstrating the performance advantages to be gained from the use of this approach.
The frequent pattern mining framework can also be applied to solve other interesting data mining applications.The task of semantic image retrieval can be turned into a frequent pattern problem by representing image data as association ratio graph.Employing this graph-based structure, an effective semantic image retrieval architecture system is proposed for mining multimedia data efficiently.

Download Full Thesis
2,495 KB
S. No. Chapter Title of the Chapters Page Size (KB)
1 0 CONTENTS

 

ix
147 KB
2

1

INTRODUCTION

1.1 The Context
1.2 Motivation
1.3 Contributions
1.4 Organization Of The Thesis

1
253 KB
3 2 PROBLEM DEFINITION

2.1 Frequent Pattern Mining
2.2 Content-based Image Retrieval

13
599 KB
4 3 RELATED WORK

3.1 Parameter-free Mining
3.2 Frequent Pattern Mining
3.3 Graph Theoretic Approaches
3.4 Content-based Image Retrieval

35
170 KB
5 4 MINING WITHOUT MINIMUM SUPPORT THRESHOLD

4.1 Association Ratio Metric
4.2 Association Ratio Graph
4.3 All Path Source-to-destination Tree
4.4 Mining Maximal Frequent Itemsets
4.5 Experimental Study
4.6 Summary

48
1,084 KB
6 5 SEMANTIC IMAGE RETRIEVAL WITH FREQUENT ITEMSET FRAMEWOR

5.1 Semantic Gap
5.2 The System Architecture
5.3 Semantic Image Retrieval With Frequent Itemsets
5.4 Experiments And Results
5.5 Summary

89
916 KB
7 6 DISCUSSION

6.1 Characteristics
6.2 Application To Other Data Mining Tasks
6.3 Summary

118
152 KB
8 7 CONCLUSION

7.1 Summary Of The Work
7.2 Future Research Directions
7.3 Final Thoughts

122
228 KB
9

8

BIBLIOGRAPHY AND APPENDIX

 

127
262 KB