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Responsive threshold search based memetic algorithm for balanced minimum sum-of-squares clustering
- Source :
- Information Sciences. 569:184-204
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Clustering is a common task in data mining for constructing well-separated groups (clusters) from a large set of data points. The balanced minimum sum-of-squares clustering problem is a variant of the classic minimum sum-of-squares clustering (MSSC) problem and arises from broad real-life applications where the cardinalities of any two clusters differ by at most one. This study presents the first memetic algorithm for solving the balanced MSSC problem. The proposed algorithm combines a backbone-based crossover operator for generating offspring solutions and a responsive threshold search that alternates between a threshold-based exploration procedure and a descent-based improvement procedure for improving new offspring solutions. Numerical results on 16 real-life datasets show that the proposed algorithm competes very favorably with several state-of-the-art methods from the literature. Key components of the proposed algorithm are investigated to understand their effects on the performance of the algorithm.
- Subjects :
- Information Systems and Management
Computer science
05 social sciences
Crossover
Explained sum of squares
050301 education
02 engineering and technology
Computer Science Applications
Theoretical Computer Science
Operator (computer programming)
Data point
Artificial Intelligence
Control and Systems Engineering
0202 electrical engineering, electronic engineering, information engineering
Key (cryptography)
Memetic algorithm
020201 artificial intelligence & image processing
Cluster analysis
0503 education
Algorithm
Software
Descent (mathematics)
Subjects
Details
- ISSN :
- 00200255
- Volume :
- 569
- Database :
- OpenAIRE
- Journal :
- Information Sciences
- Accession number :
- edsair.doi...........6f1ff75329a82740cf27e956dcfb861c