High performance clustering with differential evolution

Partitional clustering poses a NP hard search problem for non-trivial problems. While genetic algorithms (GA) have been very popular in the clustering field, particle swarm optimization (PSO) and differential evolution (DE) are rather unknown. We report results of a performance comparison between a...

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Bibliographic Details
Main Authors: Paterlini, S., Krink, T.
Format: Conference Proceeding
Language:English
Subjects:
Applied sciences
Artificial intelligence
Clustering algorithms
Computer science
control theory
systems
Encoding
Exact sciences and technology
Genetic algorithms
Partitioning algorithms
Robustness
Search problems
Shape
Simulated annealing
Space exploration
Stochastic processes
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Summary:Partitional clustering poses a NP hard search problem for non-trivial problems. While genetic algorithms (GA) have been very popular in the clustering field, particle swarm optimization (PSO) and differential evolution (DE) are rather unknown. We report results of a performance comparison between a GA, PSO and DE for a medoid evolution clustering approach. Our results show that DE is clearly and consistently superior compared to GAs and PSO, both in respect to precision and robustness of the results for hard clustering problems. We conclude that DE rather than GAs should be primarily considered for tackling partitional clustering problems with numerical optimization.
DOI:10.1109/CEC.2004.1331142