JCP 2008 Vol.3(7): 1-8 ISSN: 1796-203X
doi: 10.4304/jcp.3.7.1-8
doi: 10.4304/jcp.3.7.1-8
Discovery of Sequential Patterns Coinciding with Analysts’ Interests
Shigeaki Sakurai, Youichi Kitahara, Ryohei Orihara, Koichiro Iwata, Nobuyoshi Honda, Toshio Hayashi
1Toshiba Corporation, Japan
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
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
Cite: Shigeaki Sakurai, Youichi Kitahara, Ryohei Orihara, Koichiro Iwata, Nobuyoshi Honda, Toshio Hayashi, "Discovery of Sequential Patterns Coinciding with Analysts’ Interests," Journal of Computers vol. 3, no. 7, pp. 1-8, 2008.
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General Information
ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat,etc
E-mail: jcp@iap.org
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