Improved negative-border online mining approaches

Authors:
Ching-Yao Wang;Shian-Shyong Tseng;Tzung-Pei Hong
Affiliations:
Information & Communications Research Lab, Industrial Technology Research Institute, Hsinchu, Taiwan, R.O.C.;Department of Computer Science, National Chiao-Tung University, Hsinchu, Taiwan, R.O.C.;Department of Electrical Engineering, National University of Kaohsiung, Kaohsiung, Taiwan, R.O.C.
Venue:
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2006

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Abstract

In the past, we proposed an extended multidimensional pattern relation (EMPR) to structurally and systematically store previously mining information for each inserted block of data, and designed a negative-border online mining (NOM) approach to provide ad-hoc, query-driven and online mining supports. In this paper, we try to use appropriate data structures and design efficient algorithms to improve the performance of the NOM approach. The lattice data structure is utilized to organize and maintain all candidate itemsets such that the candidate itemsets with the same proper subsets can be considered at the same time. The derived lattice-based NOM (LNOM) approach will require only one scan of the itemsets stored in EMPR, thus saving much computation time. In addition, a hashing technique is used to further improve the performance of the NOM approach since many itemsets stored in EMPR may be useless for calculating the counts of candidates. At last, experimental results show the effect of the improved NOM approaches.