An FP-tree based approach for mining all strongly correlated item pairs

Authors:
Zengyou He;Shengchun Deng;Xiaofei Xu
Affiliations:
Department of Computer Science and Engineering, Harbin Institute of Technology, China;Department of Computer Science and Engineering, Harbin Institute of Technology, China;Department of Computer Science and Engineering, Harbin Institute of Technology, China
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

Citing 8
Cited 2

Beyond market baskets: generalizing association rules to correlations

SIGMOD '97 Proceedings of the 1997 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
Scalable Algorithms for Association Mining

IEEE Transactions on Knowledge and Data Engineering
Alternative Interest Measures for Mining Associations in Databases

IEEE Transactions on Knowledge and Data Engineering
Mining Mutually Dependent Patterns

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
CoMine: Efficient Mining of Correlated Patterns

ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Exploiting a support-based upper bound of Pearson's correlation coefficient for efficiently identifying strongly correlated pairs

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining

Association rules mining including weak-support modes using novel measures

WSEAS Transactions on Computers
Context Based Positive and Negative Spatio-Temporal Association Rule Mining

Knowledge-Based Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Based on the FP-tree data structure, this paper presents an efficient algorithm for mining the complete set of positive correlated item pairs. Our experimental results on both synthetic and real world datasets show that, the performance of our algorithm is significantly better than that of the previously developed Taper algorithm over practical ranges of correlation threshold specifications.