The difference between our clustering algorithm and batch K-means algorithm is that in our algorithm each point is not only attributable to one cluster,instead affects multiple cluster centroid values,and the degree of influence of a point on a cluster centroid depends on the distance values between this point and the other more near cluster centroids.Our algorithm and a number of different algorithms on a number of different data sets were clustered respectively from the point of view of their clustering result's five performance index values such as entropy,purity,F1 value,Rand Index and normalized mutual information,and the results show our algorithm has a better clustering results.Our algorithm and a number of different algorithms were clustered respectively on one same data set but under many different initialization conditions,and clustering results of our algorithm are preferably more stable and better.Cluster on different size data sets by our algorithm has a linear scalability and is faster.
NETL
NSTL
维普
万方数据
