A pattern decomposition algorithm for data mining of frequent patterns

Authors:
Qinghua Zou;Wesley Chu;David Johnson;Henry Chiu
Affiliations:
Department of Computer Science, University of California at Los Angeles, CA;Department of Computer Science, University of California at Los Angeles, CA;UCLA Telemedicine, CA;IBM Almaden, San Jose, CA
Venue:
Knowledge and Information Systems
Year:
2002

Citing 10
Cited 4

An effective hash-based algorithm for mining association rules

SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data

SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases

SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Algorithm 457: finding all cliques of an undirected graph

Communications of the ACM
Pincer Search: A New Algorithm for Discovering the Maximum Frequent Set

EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
A Pattern Decomposition (PD) Algorithm for Finding All Frequent Patterns in Large Datasets

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases

VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules

VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases

A new approach to mine frequent patterns using item-transformation methods

Information Systems
Drug exposure side effects from mining pregnancy data

ACM SIGKDD Explorations Newsletter - Special issue on data mining for health informatics
An ontological Proxy Agent with prediction, CBR, and RBR techniques for fast query processing

Expert Systems with Applications: An International Journal
An ontology-supported database refurbishing technique and its application in mining GSM trouble shooting rules

KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

Efficient algorithms to mine frequent patterns are crucial to many tasks in data mining. Since the Apriori algorithm was proposed in 1994, there have been several methods proposed to improve its performance. However, most still adopt its candidate set generation-and-test approach. In addition, many methods do not generate all frequent patterns, making them inadequate to derive association rules. We propose a pattern decomposition (PD) algorithm that can significantly reduce the size of the dataset on each pass, making it more efficient to mine all frequent patterns in a large dataset. The proposed algorithm avoids the costly process of candidate set generation and saves time by reducing the size of the dataset. Our empirical evaluation shows that the algorithm outperforms Apriori by one order of magnitude and is faster than FP-tree algorithm.