JOURNAL OF COMPUTERS (JCP)
ISSN : 1796-203X
Volume : 3    Issue : 7    Date : July 2008

Discovery of Sequential Patterns Coinciding with Analysts’ Interests
Shigeaki Sakurai, Youichi Kitahara, Ryohei Orihara, Koichiro Iwata, Nobuyoshi Honda, and Toshio
Hayashi
Page(s): 1-8
Full Text:
PDF (780 KB)


Abstract
This paper proposes a new sequential pattern mining method. The method introduces a new
evaluation criterion satisfying the Apriori property. The criterion is calculated by the frequency of the
sequential pattern and the minimum frequency of items included in the items. It extracts sequential
patterns that can be rules predicting future items with high probability. Also, the method introduces
new constraints. The constraints extract item sets composed of items whose attributes are different
and extracts sequential patterns composed of item sets whose attribute sets are equal to one
another. The proposed method efficiently discovers sequential patterns coinciding with analysts’
interests by combining the criterion and the constraints. The paper verifies the effectiveness of the
proposed method by applying it to medical examination data.

Index Terms
sequential data, sequential pattern mining, sequential interestingness, attribute constraint, medical
examination data