Using a cosine-type measure to derive strong association mining rules

Authors:
Sikha Bagui;Jiri Just;Subhash C. Bagui;Rohan Hemasinha
Affiliations:
Department of Computer Science, University of West Florida, Pensacola, FL 32514, USA.;Department of Computer Science, University of West Florida, Pensacola, FL 32514, USA.;Department of Mathematics and Statistics, University of West Florida, Pensacola, FL 32514, USA.;Department of Mathematics and Statistics, University of West Florida, Pensacola, FL 32514, USA
Venue:
International Journal of Knowledge Engineering and Data Mining
Year:
2010

Citing 17
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
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
Efficient mining of emerging patterns: discovering trends and differences

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Clustering Association Rules

ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Mining Sequential Patterns

ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases

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
Local and Global Methods in Data Mining: Basic Techniques and Open Problems

ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach

Data Mining and Knowledge Discovery
Pushing Convertible Constraints in Frequent Itemset Mining

Data Mining and Knowledge Discovery
Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques
Fast Algorithms for Frequent Itemset Mining Using FP-Trees

IEEE Transactions on Knowledge and Data Engineering
Introduction to Data Mining, (First Edition)

Introduction to Data Mining, (First Edition)
GenMax: An Efficient Algorithm for Mining Maximal Frequent Itemsets

Data Mining and Knowledge Discovery
Parallel FP-growth on PC cluster

PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining

Quantified Score

Hi-index	0.00

Visualization

Abstract

Association mining rule algorithms have two major drawbacks – the need to repeatedly scan the dataset and the generation of too many association rules. In this paper we present an algorithm that concentrates on addressing these drawbacks. We present a correlation based association mining rule algorithm, implemented using an arraylist structure in JAVA, that does not require more than one scan of the full dataset and generates far lot less strong association mining rules. The correlation criteria used is a cosine-type measure.