Genetic Programming for Evolving Similarity Functions for Clustering: Representations and Analysis

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@Article{Lensen:EC,

• author = "Andrew Lensen and Bing Xue and Mengjie Zhang",
• title = "Genetic Programming for Evolving Similarity Functions for Clustering: Representations and Analysis",
• journal = "Evolutionary Computation",
• year = "2020",
• volume = "28",
• number = "4",
• pages = "531--561",
• note = "Winter",
• keywords = "genetic algorithms, genetic programming, Cluster analysis, automatic clustering, similarity function, feature selection, feature construction",
• ISSN = "1063-6560",
• DOI = "doi:10.1162/evco_a_00264",
• size = "29 pages",
• abstract = "Clustering is a difficult and widely-studied data mining task, with many varieties of clustering algorithms proposed in the literature. Nearly all algorithms use a similarity measure such as a distance metric (e.g. Euclidean distance) to decide which instances to assign to the same cluster. These similarity measures are generally pre-defined and cannot be easily tailored to the properties of a particular dataset, which leads to limitations in the quality and the interpretability of the clusters produced. In this paper, we propose a new approach to automatically evolving similarity functions for a given clustering algorithm by using genetic programming. We introduce a new genetic programming-based method which automatically selects a small subset of features (feature selection) and then combines them using a variety of functions (feature construction) to produce dynamic and flexible similarity functions that are specifically designed for a given dataset. We demonstrate how the evolved s",
• notes = "Evolutionary Computation Research Group, Victoria University of Wellington,Wellington 6140, New Zealand",

}

Genetic Programming entries for Andrew Lensen Bing Xue Mengjie Zhang

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