Efficient mining of long frequent patterns from very large dense datasets

Authors:
Raj P. Gopalan;Yudho Giri Sucahyo
Affiliations:
Department of Computing, Curtin University of Technology, Kent St, Bentley, Western Australia;Department of Computing, Curtin University of Technology, Kent St, Bentley, Western Australia
Venue:
Design and application of hybrid intelligent systems
Year:
2003

Citing 8
Cited 1

Mining association rules between sets of items in large databases

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
A tree projection algorithm for generation of frequent item sets

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Towards long pattern generation in dense databases

ACM SIGKDD Explorations Newsletter
Scalable Algorithms for Association Mining

IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
CT-ITL: efficient frequent item set mining using a compressed prefix tree with pattern growth

ADC '03 Proceedings of the 14th Australasian database conference - Volume 17
Constraint-Based Rule Mining in Large, Dense Databases

ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach

Data Mining and Knowledge Discovery

A bottom-up projection based algorithm for mining high utility itemsets

AIDM '07 Proceedings of the 2nd international workshop on Integrating artificial intelligence and data mining - Volume 84

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Abstract

Discovering association rules that identify relationships among sets of items in a transaction database is an important problem in Data Mining. Finding frequent itemsets has been an active research area since it is the crucial step in association rule discovery. However, efficiently mining frequent itemsets from dense datasets is still a challenging problem. In this paper, we describe a new and more efficient algorithm named CT-GIN for mining complete frequent itemsets from dense datasets. The algorithm uses a compact prefix tree for succinctly representing transaction data and an item group intersection method for efficient extraction of frequent itemsets from the tree. Performance comparisons show that our algorithm outperforms the fastest Apriori algorithm, Eclat and FP-Growth, on several widely used test data sets. CT-GIN has been extended for mining very large datasets, and we also present test results showing its scalability.