Cluster analysis based on cultural algorithms
基于文化算法的聚类分析

After analyzing the disadvantages of the classical K-means clustering algorithm, a new clustering algorithm based on cultural algorithms was proposed, and two different versions of implementations named CA-versionl and CA-version2 were put forward. CA-version1 uses situational knowledge to control the direction of mutation, and uses normative knowledge to control the step size of mutation. CA-version2 uses normative knowledge to control the step size and the direction of mutation. Cultural algorithms are dual inheritance systems which are different from the others. Because of this feature, the search process is guided by using knowledge abstained from the process of solving problem, which can produce substantial performance improvements. Compared with the classical K-means clustering algorithm, the algorithms based on cultural algorithms, proved by the experimental results, can not only avoid the disadvantages of the classical K-means clustering algorithm, but also have greater searching capability globally than genetic clustering algorithm. Besides, it shows that CA- version2 is more suitable than CA-versionl for clustering problem.