Stefan Mutter

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Association rule mining is a data mining technique that reveals interesting relationships in a database. Existing approaches employ different parameters to search for interesting rules. This fact and the large number of rules make it difficult to compare the output of confidence-based association rule miners. This paper explores the use of classification(More)
Further I declare that I worked autonomously and only used the stated resources. All excerpts cited from publications or unpublished scripts are indicated. Hamilton, the 11th March 2004 We are drowning in information, but starving for knowledge. Abstract Association rule mining is a well-known technique in data mining. It is able to reveal all interesting(More)
Hidden Markov Models are a widely used generative model for analysing sequence data. A variant, Profile Hidden Markov Models are a special case used in Bioinformatics to represent, for example, protein families. In this paper we introduce a simple propositionalisation method for Profile Hidden Markov Models. The method allows the use of PHMMs(More)
Multiple sequence alignments play a central role in Bioin-formatics. Most alignment representations are designed to facilitate knowledge extraction by human experts. Additionally statistical models like Profile Hidden Markov Models are used as representations. They offer the advantage to provide sound, probabilistic scores. The basic idea we present in this(More)
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