Clustering with a genetically optimized approach

Describes a genetically guided approach to optimizing the hard (J/sub 1/) and fuzzy (J/sub m/) c-means functionals used in cluster analysis. Our experiments show that a genetic algorithm (GA) can ameliorate the difficulty of choosing an initialization for the c-means clustering algorithms. Experiments use six data sets, including the Iris data, magnetic resonance, and color images. The genetic algorithm approach is generally able to find the lowest known J/sub m/ value or a J/sub m/ associated with a partition very similar to that associated with the lowest J/sub m/ value. On data sets with several local extrema, the GA approach always avoids the less desirable solutions. Degenerate partitions are always avoided by the GA approach, which provides an effective method for optimizing clustering models whose objective function can be represented in terms of cluster centers. A series random initializations of fuzzy/hard c-means, where the partition associated with the lowest J/sub m/ value is chosen, can produce an equivalent solution to the genetic guided clustering approach given the same amount of processor time in some domains.