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Title: Summarization Techniques for Pattern Collections in Data Mining

Citation Type: Dissertation/Thesis

Publication Year: 2005

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Abstract: Discovering 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.

Url: http://arxiv.org/abs/cs/0505071

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Authors: Mielikäinen, Taneli

Institution: University of Helsinki

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Pages: 202

Data Collections: IPUMS USA

Topics: Population Data Science

Countries: United States