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Cursus: INFOMPSM
INFOMPSM
Pattern set mining
Cursus informatie
CursuscodeINFOMPSM
Studiepunten (EC)7,5
Inhoud
Pattern mining is characteristic for data mining. Whereas data analysis is usually concerned with models – i.e., succinct descriptions of all data – pattern mining is about local phenomena. Patterns describe – or even are – subgroups of the data that for some reason are deemed interesting; a description and a reason that usually involves some – if any -- of the variables (attributes features) rather than all. In the past few decades – the total existence of data mining – pattern mining has proven to be a fruitful research area with many thousands of papers describing a wide variety of pattern languages, interestingness functions, and even more algorithms to discover them. However, there is a problem with pattern mining. Databases tend to exhibit many, very many patterns. It is not uncommon that one discovers more patterns than one has data. Hardly an ideal situation. Hence, the rise of pattern set mining. Can we define and find relatively small, good sets of patterns? In this course we’ll start with a brief discussion of pattern mining. After that we discuss parts of the literature on pattern set mining; only parts because there is too much to discuss it all. What types of solutions have been proposed? How do they work and, actually, do the work?
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