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Clustering rules: A comparison of partitioning and hierarchical clustering algorithms. (English) Zbl 1104.62073
Summary: Previous research has resulted in a number of different algorithms for rule discovery. Two approaches discussed here, the ‘all-rules’ algorithm and multi-objective metaheuristics, both result in the production of a large number of partial classification rules, or ‘nuggets’, for describing different subsets of the records in the class of interest. This paper describes the application of a number of different clustering algorithms to these rules, in order to identify similar rules and to better understand the data.

62H30 Classification and discrimination; cluster analysis (statistical aspects)
68T05 Learning and adaptive systems in artificial intelligence
68T37 Reasoning under uncertainty in the context of artificial intelligence
90C59 Approximation methods and heuristics in mathematical programming
clusfind; MOCK; UCI-ml
Full Text: DOI
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