Improving K-means Clustering with Genetic Programming for Feature Construction

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Lensen:2017:GECCOa,

• author = "Andrew Lensen and Bing Xue and Mengjie Zhang",
• title = "Improving K-means Clustering with Genetic Programming for Feature Construction",
• booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion",
• series = "GECCO '17",
• year = "2017",
• isbn13 = "978-1-4503-4939-0",
• address = "Berlin, Germany",
• pages = "237--238",
• size = "2 pages",
• URL = "http://doi.acm.org/10.1145/3067695.3075962",
• DOI = "doi:10.1145/3067695.3075962",
• acmid = "3075962",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, cluster analysis, evolutionary computation, feature construction, k-means",
• month = "15-19 " # jul,
• abstract = "k-means is one of the most commonly used clustering algorithms in data mining. Despite this, it has a number of fundamental limitations which prevent it from performing effectively on large or otherwise difficult datasets. A common technique to improve performance of data mining algorithms is feature construction, a technique which combines features together to produce more powerful constructed features that can improve the performance of a given algorithm. Genetic Programming (GP) has been used for feature construction very successfully, due to its program-like structure. This paper proposes two representations for using GP to perform feature construction to improve the performance of k-means, using a wrapper approach. Our results show significant improvements in performance compared to k-means using all original features across six difficult datasets.",
• notes = "Also known as \cite{Lensen:2017:IKM:3067695.3075962} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)",

}

Genetic Programming entries for Andrew Lensen Bing Xue Mengjie Zhang

Citations