Evaluating accuracies of a trading rule mining method based on temporal pattern extraction

Hidenao Abe, Satoru Hirabayashi, Miho Ohsaki, Takahira Yamaguchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we present an evaluation of accuracies of temporal rules obtained from the integrated temporal data mining environment using trading dataset from the Japanese stock market. Temporal data mining is one of key issues to get useful knowledge from databases. However, users often face on difficulties during such temporal data mining process for data pre-processing method selection/construction, mining algorithm selection, and post-processing to refine the data mining process. To get rules that are more valuable for domain experts from a temporal data mining process, we have designed an environment, which integrates temporal pattern extraction methods, rule induction methods and rule evaluation methods with visual human-system interface. Then, we have done a case study to mine temporal rules from a Japanese stock market database for trading. The result shows the availability to find out useful trading rules based on temporal pattern extraction.

Original languageEnglish
Title of host publicationMining Complex Data - ECML/PKDD 2007 Third International Workshop, MCD 2007, Revised Selected Papers
Number of pages10
Publication statusPublished - 2008
Event3rd International Workshop on Mining Complex Data, MCD 2007 - Warsaw, Poland
Duration: 2007 Sept 172007 Sept 21

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4944 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other3rd International Workshop on Mining Complex Data, MCD 2007

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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