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Temporal Associative Classification based on Calendar Patterns  

Lee Heon Gyu (충북대학교 전자계산학과)
Noh Gi Young (한국표준과학연구원)
Seo Sungbo (충북대학교 전산학과)
Ryu Keun Ho (충북대학교 컴퓨터과학과)
Publication Information
Journal of KIISE:Databases / v.32, no.6, 2005 , pp. 567-584 More about this Journal
Abstract
Temporal data mining, the incorporation of temporal semantics to existing data mining techniques, refers to a set of techniques for discovering implicit and useful temporal knowledge from temporal data. Association rules and classification are applied to various applications which are the typical data mining problems. However, these approaches do not consider temporal attribute and have been pursued for discovering knowledge from static data although a large proportion of data contains temporal dimension. Also, data mining researches from temporal data treat problems for discovering knowledge from data stamped with time point and adding time constraint. Therefore, these do not consider temporal semantics and temporal relationships containing data. This paper suggests that temporal associative classification technique based on temporal class association rules. This temporal classification applies rules discovered by temporal class association rules which extends existing associative classification by containing temporal dimension for generating temporal classification rules. Therefore, this technique can discover more useful knowledge in compared with typical classification techniques.
Keywords
Temporal data mining; Temporal Associative classification; Temporal Class Association rules;
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