An Combination Clustering Method Based on Sampling Using Approximate Aggregation
Xinquan Chen · 2008
FCM clustering algorithm has a linear time complexity,but it is sensitive to initialization.The k-medoids substitution clustering method has better clustering effect and less sensitivity to an initialized medoids set than k-means when clustering those sets of data points with some similar-size clusters.But its time complexity is too high,so it can not be used in huge amounts of data sets.In order to solve their shortcomings,a combination clustering method based on sampling using approximate aggregation is presented naturally.This method needs.This combination clustering method can make a stable clustering effect.