A genetic clustering algorithm for data with non-spherical-shape clusters

✍ Scribed by Lin Yu Tseng; Shiueng Bien Yang

• Publisher: Elsevier Science
• Year: 2000
• Tongue: English
• Weight: 266 KB
• Volume: 33
• Category: Article
• ISSN: 0031-3203
• DOI: 10.1016/s0031-3203(99)00105-3

✦ Synopsis

In solving clustering problem, traditional methods, for example, the K-means algorithm and its variants, usually ask the user to provide the number of clusters. Unfortunately, the number of clusters in general is unknown to the user. The traditional neighborhood clustering algorithm usually needs the user to provide a distance d for the clustering. This d is di$cult to decide because some clusters may be compact but others may be loose. In this paper, we propose a genetic clustering algorithm for clustering the data whose clusters are not of spherical shape. It can automatically cluster the data according to the similarities and automatically "nd the proper number of clusters. The experimental results are given to illustrate the e!ectiveness of the genetic algorithm.