Genetic Programming for Evolving Similarity Functions for Clustering: Representations and Analysis
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- @Article{Lensen:EC,
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author = "Andrew Lensen and Bing Xue and Mengjie Zhang",
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title = "Genetic Programming for Evolving Similarity Functions
for Clustering: Representations and Analysis",
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journal = "Evolutionary Computation",
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year = "2020",
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volume = "28",
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number = "4",
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pages = "531--561",
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note = "Winter",
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keywords = "genetic algorithms, genetic programming, Cluster
analysis, automatic clustering, similarity function,
feature selection, feature construction",
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ISSN = "1063-6560",
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DOI = "doi:10.1162/evco_a_00264",
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size = "29 pages",
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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",
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notes = "Evolutionary Computation Research Group, Victoria
University of Wellington,Wellington 6140, New Zealand",
- }
Genetic Programming entries for
Andrew Lensen
Bing Xue
Mengjie Zhang
Citations