Extended negative association rules and the corresponding mining algorithm

Authors:
Min Gan;Mingyi Zhang;Shenwen Wang
Affiliations:
Department of Computer, Zhejiang Water Conservancy and Hydropower College, Hangzhou, Zhejiang, P.R. China;Guizhou Academy of Sciences, Guiyang, Guizhou, P.R. China;School of Computer Science and Engineering, Guizhou University, Guiyang, Guizhou, P.R. China
Venue:
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Year:
2005

Citing 7
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
Beyond market baskets: generalizing association rules to correlations

SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining Both Positive and Negative Association Rules

ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Mining for Strong Negative Associations in a Large Database of Customer Transactions

ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach

Data Mining and Knowledge Discovery
Efficient mining of both positive and negative association rules

ACM Transactions on Information Systems (TOIS)

Mining Both Positive and Negative Association Rules from Frequent and Infrequent Itemsets

ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recently, negative association rule mining has received some attention and proved to be useful. This paper proposes an extended form for negative association rules and defines extended negative association rules. Furthermore, a corresponding algorithm is devised for mining extended negative association rules. The extended form is more general and expressive than the three existing forms. The proposed mining algorithm overcomes some limitations of previous mining methods, and experimental results show that it is efficient on simple and sparse datasets when minimum support is high to some degree. Our work will extend related applications of negative association rules to a broader range.