A Hybrid Hierarchical k-means Clustering Algorithm
Huiping Chen
Abstract
Huiping Chen
Abstract
In order to obtain better clustering results,after analyzing the advantages and disadvantages of hierarchical and k-means clustering algorithms,a new algorithm which combines the advantages of hierarchical and k-means clustering algorithms is proposed.In the algorithm,hierarchical clustering is carried out at first to get an initial clustering in the first round and then the k-means clustering is carried out in another round.The results of experiment suggest that this new method has faster speed,higher efficiency and better clustering results than previous traditional clustering algorithms.
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In order to obtain better clustering results,after analyzing the advantages and disadvantages of hierarchical and k-means clustering algorithms,a new algorithm which combines the advantages of hierarchical and k-means clustering algorithms is proposed.In the algorithm,hierarchical clustering is carried out at first to get an initial clustering in the first round and then the k-means clustering is carried out in another round.The results of experiment suggest that this new method has faster speed,higher efficiency and better clustering results than previous traditional clustering algorithms.
Key concepts: Cluster analysis, CURE data clustering algorithm, Canopy clustering algorithm, Correlation clustering, Hierarchical clustering, Single-linkage clustering, Fuzzy clustering, Computer science