An efficient approach to mine rare association rules using maximum items' support constraints

Authors:
R. Uday Kiran;Polepalli Krishna Reddy
Affiliations:
Center for Data Engineering, International Institute of Information Technology-Hyderabad, Hyderabad, Andhra Pradesh, India;Center for Data Engineering, International Institute of Information Technology-Hyderabad, Hyderabad, Andhra Pradesh, India
Venue:
BNCOD'10 Proceedings of the 27th British national conference on Data Security and Security Data
Year:
2010

Citing 11
Cited 0

Mining association rules between sets of items in large databases

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Using association rules for product assortment decisions: a case study

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining association rules with multiple minimum supports

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithms for association rule mining — a general survey and comparison

ACM SIGKDD Explorations Newsletter
Data Mining and Machine Oriented Modeling: A Granular Computing Approach

Applied Intelligence
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach

Data Mining and Knowledge Discovery
Introduction to the special issue on successful real-world data mining applications

ACM SIGKDD Explorations Newsletter
Mining association rules with multiple minimum supports: a new mining algorithm and a support tuning mechanism

Decision Support Systems
Association rule and quantitative association rule mining among infrequent items

Proceedings of the 8th international workshop on Multimedia data mining: (associated with the ACM SIGKDD 2007)
Mining association rules with multiple minimum supports using maximum constraints

International Journal of Approximate Reasoning
Mining rare association rules in the datasets with widely varying items' frequencies

DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

Rare association rule is an association rule consisting of rare items. It is difficult to mine rare association rules with a single minimum support (minsup) constraint because low minsup can result in generating too many rules (or frequent patterns) in which some of them are uninteresting. In the literature, "maximum constraint model," which uses multiple minsup constraints has been proposed and extended to Apriori approach for mining frequent patterns. Even though this model is efficient, the Apriori-like approach raises performance problems. With this motivation, we propose an FP-growth-like approach for this model. This FP-growth-like approach utilizes the prior knowledge provided by the user at the time of input and discovers frequent patterns with a single scan on the transactional dataset. Experimental results on both synthetic and real-world datasets show that the proposed approach is efficient.