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ÐWith the increase in the size of data sets, data mining has recently become an important research topic and is receiving substantial interest from both academia and industry. At the same time, interest in temporal databases has been increasing and a growing number of both prototype and implemented systems are using an enhanced temporal understanding to explain aspects of behavior associated with the implicit time-varying nature of the universe. This paper investigates the confluence of these two areas, surveys the work to date, and explores the issues involved and the outstanding problems in temporal data mining. Index TermsÐTemporal data mining, time sequence mining, trend analysis, temporal rules, semantics of mined rules.

The task of finding correlations between items in a dataset, association mining, has received considerable attention over the last decade. This article presents a survey of association mining fundamentals, detailing the evolution of association mining algorithms from the seminal to the state-of-the-art. This survey focuses on the fundamental principles of association mining, that is, itemset identification, rule generation, and their generic optimizations.

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Sequences of events, items, or tokens occurring in an ordered metric space appear often in data and the requirement to detect and analyze frequent subsequences is a common problem. Sequential Pattern Mining arose as a subfield of data mining to focus on this field. This article surveys the approaches and algorithms proposed to date.