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Applied Computational Intelligence and Soft Computing
Volume 2012, Article ID 786387, 12 pages
Research Article

Discovery of Characteristic Patterns from Transactions with Their Classes

1Business Intelligence Laboratory and Advanced IT Laboratory, Toshiba Solutions Corporation, Tokyo 183-8512, Japan
2Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Kanagawa 226-8502, Japan

Received 21 October 2011; Revised 31 December 2011; Accepted 15 January 2012

Academic Editor: Tzung P. Hong

Copyright © 2012 Shigeaki Sakurai. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper deals with transactions with their classes. The classes represent the difference of conditions in the data collection. This paper redefines two kinds of supports: characteristic support and possible support. The former one is based on specific classes assigned to specific patterns. The latter one is based on the minimum class in the classes. This paper proposes a new method that efficiently discovers patterns whose characteristic supports are larger than or equal to the predefined minimum support by using their possible supports. Also, this paper verifies the effect of the method through numerical experiments based on the data registered in the UCI machine learning repository and the RFID (radio frequency identification) data collected from two apparel shops.