Discovery of Sequential Patterns Coinciding with Analysts’ Interests

Autor:	Youichi Kitahara, Nobuyoshi Honda, Koichiro Iwata, Ryohei Orihara, Shigeaki Sakurai, Toshio Hayashi
Rok vydání:	2008
Předmět:	High probability General Computer Science Computer science Property (programming) business.industry Pattern recognition computer.software_genre A priori and a posteriori Sequential data Data mining Artificial intelligence Sequential Pattern Mining business computer
Zdroj:	Journal of Computers. 3
ISSN:	1796-203X
DOI:	10.4304/jcp.3.7.1-8
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::73788955460fefa1c7569340a3e25862 https://doi.org/10.4304/jcp.3.7.1-8 Zobrazit plný text záznamu