Genetic algorithm-based clustering technique
Abstract
A genetic algorithm-based clustering technique, called GA-clustering, is proposed in this article. The searching capability of genetic algorithms is exploited in order to search for appropriate cluster centres in the feature space such that a similarity metric of the resulting clusters is optimized. The chromosomes, which are represented as strings of real numbers, encode the centres of a fixed number of clusters. The superiority of the GA-clustering algorithm over the commonly used K-means algorithm is extensively demonstrated for four artificial and three real-life data sets.
- Publication:
-
Pattern Recognition
- Pub Date:
- 2000
- DOI:
- 10.1016/S0031-3203(99)00137-5
- Bibcode:
- 2000PatRe..33.1455M
- Keywords:
-
- Genetic algorithms;
- Clustering metric;
- K-means algorithm;
- Real encoding;
- Euclidean distance