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Automatic clustering by multi-objective genetic algorithm with numeric and categorical features
- Source :
- Expert Systems with Applications. 137:357-379
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- 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.
- Subjects :
- 0209 industrial biotechnology
Optimization problem
Computer science
General Engineering
02 engineering and technology
Computer Science Applications
ComputingMethodologies_PATTERNRECOGNITION
020901 industrial engineering & automation
Local optimum
Artificial Intelligence
Genetic algorithm
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Cluster analysis
Categorical variable
Algorithm
Linear separability
Curse of dimensionality
Subjects
Details
- ISSN :
- 09574174
- Volume :
- 137
- Database :
- OpenAIRE
- Journal :
- Expert Systems with Applications
- Accession number :
- edsair.doi...........4bb412b9618385ba93e567f468c6300c