Mining Weighted Frequent Patterns Using Adaptive Weights

Authors:
Chowdhury Farhan Ahmed;Syed Khairuzzaman Tanbeer;Byeong-Soo Jeong;Young-Koo Lee
Affiliations:
Department of Computer Engineering, Kyung Hee University, Kyunggi-do, Republic of Korea 446-701;Department of Computer Engineering, Kyung Hee University, Kyunggi-do, Republic of Korea 446-701;Department of Computer Engineering, Kyung Hee University, Kyunggi-do, Republic of Korea 446-701;Department of Computer Engineering, Kyung Hee University, Kyunggi-do, Republic of Korea 446-701
Venue:
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Year:
2008

Citing 10
Cited 0

Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Post-mining: maintenance of association rules by wieghting

Information Systems
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach

Data Mining and Knowledge Discovery
Weighted Association Rule Mining using weighted support and significance framework

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
WAR: Weighted Association Rules for Item Intensities

Knowledge and Information Systems
Research issues in data stream association rule mining

ACM SIGMOD Record
DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams

ICDM '06 Proceedings of the Sixth International Conference on Data Mining
CanTree: a canonical-order tree for incremental frequent-pattern mining

Knowledge and Information Systems
Efficient mining of weighted interesting patterns with a strong weight and/or support affinity

Information Sciences: an International Journal
CP-tree: a tree structure for single-pass frequent pattern mining

PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining

Quantified Score

Hi-index	0.00

Visualization

Abstract

Existing weighted frequent pattern (WFP) mining algorithms assume that each item has fixed weight. But in our real world scenarios the weight (price or significance) of an item can vary with time. Reflecting such change of weight of an item is very necessary in several mining applications such as retail market data analysis and web click stream analysis. In this paper, we introduce a novel concept of adaptive weight for each item and propose an algorithm AWFPM (adaptive weighted frequent pattern mining). Our algorithm can handle the situation where the weight (price or significance) of an item may vary with time. Extensive performance analyses show that our algorithm is very efficient and scalable for WFP mining using adaptive weights.