Enumeration tree based emerging patterns mining by using two different supports

Authors:
Minghao Piao;Jong Bum Lee;Ho Sun Shon;Unil Yun;Keun Ho Ryu
Affiliations:
Database, Bioinformatics Lab., Chungbuk National University, Cheonghu, South Korea;Database, Bioinformatics Lab., Chungbuk National University, Cheonghu, South Korea;Database, Bioinformatics Lab., Chungbuk National University, Cheonghu, South Korea;Department of Computer Science, Chungbuk National University, Korea;Database, Bioinformatics Lab., Chungbuk National University, Cheonghu, South Korea
Venue:
ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
Year:
2011

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Abstract

Recently, the analysis of power load in the electrical industry has becomes an important element for the concern of customer safety. In power system related studies, data mining techniques are used in power load analysis and they can help decision making in the electrical industry. In this paper, for using emerging patterns to define and analyze the significant difference of safe and non-safe power load lines, and identifying which line is potentially unsafe, we proposed an incremental TFP-tree algorithm for mining emerging patterns that can search efficiently within memory limitation. Especially, the use of two different minimum supports makes the algorithm possible to mine most number of emerging patterns and efficiently handle the incrementally increased, large size of data sets such as power consumption data.