Mining Supplemental Frequent Patterns

Authors:
Yintian Liu;Yingming Liu;Tao Zeng;Kaikuo Xu;Rong Tang
Affiliations:
College of Mathematics, Sichuan University, Chengdu, China 610065 and DB&KE Lab., Chengdu University of Information Technology, Chengdu, China 610225;College of Mathematics, Sichuan University, Chengdu, China 610065;Tianjin Normal University, Tianjin, China 300074;College of Mathematics, Sichuan University, Chengdu, China 610065;College of Mathematics, Sichuan University, Chengdu, China 610065
Venue:
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Year:
2008

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Abstract

The process of resource distribution and load balance of a distributed P2P network can be described as the process of mining Supplement Frequent Patterns (SFPs) from query transaction database. With given minimum support (min_sup) and minimum share support (min_share_sup), each SFP includes a core frequent pattern (BFP) used to draw other frequent or sub-frequent items. A latter query returns a subset of a SFP as the result. To realize the SFPs mining, this paper proposes the structure of SFP-tree along with relative mining algorithms. The main contribution includes: (1) Describes the concept of Supplement Frequent Pattern; (2) Proposes the SFP-tree along with frequency-Ascending order header table FP-Tree (AFP-Tree) and Conditional Mix Pattern Tree (CMP-Tree); (3) Proposes the SFPs mining algorithms based on SFP-Tree; and (4) Conducts the performance experiment on both synthetic and real datasets. The result shows the effectiveness and efficiency of the SFPs mining algorithm based on SFP-Tree.