Automatic clustering by multi-objective genetic algorithm with numeric and categorical features

•We have developed a clustering algorithm for an unknown number of clusters by MOGA.•It works with continuous and categorical featured data sets.•It can work with data sets having missing values.•The final solution is selected by majority vote by all non-dominated solutions.•Context-sensitive and cl...

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Vydané v:Expert systems with applications Ročník 137; s. 357 - 379
Hlavní autori: Dutta, Dipankar, Sil, Jaya, Dutta, Paramartha
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Elsevier Ltd 15.12.2019
Elsevier BV
Predmet:
Automatic clustering
Centroids
Clustering
Data points
Genetic algorithms
Iterative methods
Machine learning
Multi-Objective Genetic Algorithm (MOGA)
Multiple objective analysis
Optimization
Pareto approach
Statistical test
Statistical test
Automatic clustering
Multi-Objective Genetic Algorithm (MOGA)
Pareto approach
ISSN:0957-4174, 1873-6793
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Shrnutí:•We have developed a clustering algorithm for an unknown number of clusters by MOGA.•It works with continuous and categorical featured data sets.•It can work with data sets having missing values.•The final solution is selected by majority vote by all non-dominated solutions.•Context-sensitive and cluster-orient genetic operators are designed. Many clustering algorithms categorized as K-clustering algorithm require the user to predict the number of clusters (K) to do clustering. Due to lack of domain knowledge an accurate value of K is difficult to predict. The problem becomes critical when the dimensionality of data points is large; clusters differ widely in shape, size, and density; and when clusters are overlapping in nature. Determining the suitable K is an optimization problem. Automatic clustering algorithms can discover the optimal K. This paper presents an automatic clustering algorithm which is superior to K-clustering algorithm as it can discover an optimal value of K. Iterative hill-climbing algorithms like K-Means work on a single solution and converge to a local optimum solution. Here, Genetic Algorithms (GAs) find out near global optimum solutions, i.e. optimal K as well as the optimal cluster centroids. Single-objective clustering algorithms are adequate for efficiently grouping linearly separable clusters. For non-linearly separable clusters they are not so good. So for grouping non-linearly separable clusters, we apply Multi-Objective Genetic Algorithm (MOGA) by minimizing the intra-cluster distance and maximizing inter-cluster distance. Many existing MOGA based clustering algorithms are suitable for either numeric or categorical features. This paper pioneered employing MOGA for automatic clustering with mixed types of features. Statistical testing on experimental results on real-life benchmark data sets from the University of California at Irvine (UCI) machine learning repository proves the superiority of the proposed algorithm.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2019.06.056