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International Workshop on Knowledge Discovery in Inductive Databases

KDID 2004: Knowledge Discovery in Inductive Databases pp 130–149Cite as

An Automata Approach to Pattern Collections

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3377))

Abstract

Condensed representations of pattern collections have been recognized to be important building blocks of inductive databases, a promising theoretical framework for data mining, and recently they have been studied actively. However, there has not been much research on how condensed representations should actually be represented.

In this paper we study how condensed representations of frequent itemsets can be concretely represented: we propose the use of deterministic finite automata to represent pattern collections and study the properties of the automata representation. The automata representation supports visualization of the patterns in the collection and clustering of the patterns based on their structural properties and interestingness values. Furthermore, we show experimentally that finite automata provide a space-efficient way to represent itemset collections.

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Authors and Affiliations

  1. HIIT Basic Research Unit, Department of Computer Science, University of Helsinki, Finland

    Taneli Mielikäinen

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  1. Taneli Mielikäinen
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Editors and Affiliations

  1. Mathematics and computer Science Department, University of Antwerp, Middelheimlaan 1, 2020, Antwerp, Belgium

    Bart Goethals

  2. Department of Computer Science, Universiteit Utrecht,  

    Arno Siebes

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Mielikäinen, T. (2005). An Automata Approach to Pattern Collections. In: Goethals, B., Siebes, A. (eds) Knowledge Discovery in Inductive Databases. KDID 2004. Lecture Notes in Computer Science, vol 3377. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31841-5_8

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  • DOI: https://doi.org/10.1007/978-3-540-31841-5_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25082-1

  • Online ISBN: 978-3-540-31841-5

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