Temporal Data Mining Framework 


Vol. 9,  No. 3, pp. 365-380, Jun.  2002
10.3745/KIPSTD.2002.9.3.365


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  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 large quantities of temporal data. Temporal knowledge, expressible in the form of rules, is knowledge with temporal semantics and relationships, such as cyclic pattern, calendric pattern, trends, etc. There are many examples of temporal data, including patient histories, purchaser histories, and web log that it can discover useful temporal knowledge from. Many studies on data mining have been pursued and some of them have involved issues of temporal data mining for discovering temporal knowledge from temporal data, such as sequential pattern, similar time sequence, cyclic and temporal association rules, etc. However, all of the works treated data in database at best as data series in chronological order and did not consider temporal semantics and temporal relationships containing data. In order to solve this problem, we propose a theoretical framework for temporal data mining. This paper surveys the work to date and explores the issues involved in temporal data mining. We then define a model for temporal data mining and suggest SQL-like mining language with ability to express the task of temporal mining and show architecture of temporal mining system.

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[IEEE Style]

J. W. Lee, Y. J. Lee, K. H. Ryu, "Temporal Data Mining Framework," The KIPS Transactions:PartD, vol. 9, no. 3, pp. 365-380, 2002. DOI: 10.3745/KIPSTD.2002.9.3.365.

[ACM Style]

Jun Wook Lee, Yong Joon Lee, and Keun Ho Ryu. 2002. Temporal Data Mining Framework. The KIPS Transactions:PartD, 9, 3, (2002), 365-380. DOI: 10.3745/KIPSTD.2002.9.3.365.

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