Differential evolution with competing strategies applied to partitional clustering

Authors:
Josef Tvrdík;Ivan Křivý
Affiliations:
Institute for Research and Applications of Fuzzy Modeling, Centre of Excellence IT4Innovations, Division of University of Ostrava, Ostrava, Czech Republic;Institute for Research and Applications of Fuzzy Modeling, Centre of Excellence IT4Innovations, Division of University of Ostrava, Ostrava, Czech Republic
Venue:
SIDE'12 Proceedings of the 2012 international conference on Swarm and Evolutionary Computation
Year:
2012

Citing 13
Cited 0

Data clustering: a review

ACM Computing Surveys (CSUR)
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Journal of Global Optimization
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)

Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)

Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
Influence of crossover on the behavior of Differential Evolution Algorithms

Applied Soft Computing
Differential evolution and particle swarm optimisation in partitional clustering

Computational Statistics & Data Analysis
Recent advances in differential evolution: a survey and experimental analysis

Artificial Intelligence Review
Kernel-induced fuzzy clustering of image pixels with an improved differential evolution algorithm

Information Sciences: an International Journal
A clustering method combining differential evolution with the K-means algorithm

Pattern Recognition Letters
Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems

IEEE Transactions on Evolutionary Computation
Differential Evolution: A Survey of the State-of-the-Art

IEEE Transactions on Evolutionary Computation
Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters

IEEE Transactions on Evolutionary Computation
Automatic Clustering Using an Improved Differential Evolution Algorithm

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider the problem of optimal partitional clustering of real data sets by optimizing three basic criteria (trace of within scatter matrix, variance ratio criterion, and Marriottt's criterion). Four variants of the algorithm based on differential evolution with competing strategies are compared on eight real-world data sets. The experimental results showed that hybrid variants with k-means algorithm for a local search are essentially more efficient than the others. However, the use of Marriottt's criterion resulted in stopping hybrid variants at a local minimum.