New Representations in Genetic Programming for Feature Construction in k-Means Clustering
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{lensen2017new,
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author = "Andrew Lensen and Bing Xue and Mengjie Zhang",
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title = "New Representations in Genetic Programming for Feature
Construction in k-Means Clustering",
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booktitle = "Proceedings of the 11th International Conference on
Simulated Evolution and Learning, SEAL-2017",
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year = "2017",
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editor = "Yuhui Shi and Kay Chen Tan and Mengjie Zhang and
Ke Tang and Xiaodong Li and Qingfu Zhang and Ying Tan and
Martin Middendorf and Yaochu Jin",
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volume = "10593",
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series = "Lecture Notes in Computer Science",
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pages = "543--555",
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address = "Shenzhen, China",
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month = nov # " 10-13",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, Cluster
analysis, Feature construction, k-means, Evolutionary
computation",
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isbn13 = "978-3-319-68759-9",
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URL = "https://doi.org/10.1007/978-3-319-68759-9_44",
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DOI = "doi:10.1007/978-3-319-68759-9_44",
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abstract = "k-means is one of the fundamental and most well-known
algorithms in data mining. It has been widely used in
clustering tasks, but suffers from a number of
limitations on large or complex datasets. Genetic
Programming (GP) has been used to improve performance
of data mining algorithms by performing feature
construction the process of combining multiple
attributes (features) of a dataset together to produce
more powerful constructed features. In this paper, we
propose novel representations for using GP to perform
feature construction to improve the clustering
performance of the k-means algorithm. Our experiments
show significant performance improvement compared to
k-means across a variety of difficult datasets. Several
GP programs are also analysed to provide insight into
how feature construction is able to improve clustering
performance.",
- }
Genetic Programming entries for
Andrew Lensen
Bing Xue
Mengjie Zhang
Citations