Summarization Techniques for Pattern Collections in Data Mining

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dc.creatorMielikäinen, Taneli
dc.date2005-05-26
dc.date.accessioned2026-07-07T03:23:03Z
dc.date.available2026-07-07T03:23:03Z
dc.descriptionDiscovering patterns from data is an important task in data mining. There exist techniques to find large collections of many kinds of patterns from data very efficiently. A collection of patterns can be regarded as a summary of the data. A major difficulty with patterns is that pattern collections summarizing the data well are often very large. In this dissertation we describe methods for summarizing pattern collections in order to make them also more understandable. More specifically, we focus on the following themes: 1) Quality value simplifications. 2) Pattern orderings. 3) Pattern chains and antichains. 4) Change profiles. 5) Inverse pattern discovery.
dc.descriptionPhD Thesis, Department of Computer Science, University of Helsinki
dc.identifierhttps://arxiv.org/abs/cs/0505071
dc.identifierhttp://arxiv.org/abs/cs/0505071
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/32791
dc.subjectDatabases
dc.subjectArtificial Intelligence
dc.subjectData Structures and Algorithms
dc.subjectE.4; F.2; H.2.8; I.2; I.2.4
dc.titleSummarization Techniques for Pattern Collections in Data Mining
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