Improved K-Modes Clustering Algorithm Based on Rough Sets
Fuyuan Cao · 2009
Traditional K-Modes clustering algorithm uses a simple matching dissimilarity measure to compute the distance between two objects.However,the similarity between two values of the same attributes is not considered.A new distance measure based on upper and lower approximations in rough set theory was proposed,and a new description of cluster center was defined.Traditional K-Modes clustering algorithm was improved.By comparing with other improved K-Modes algorithms,experimental results illustrate that the improved K-Modes clustering algorithm based on rough sets increases the clustering accuracy.