2007Journal of Hohai University ChangzhouRequires access

A Hybrid Hierarchical k-means Clustering Algorithm

Huiping Chen

Open publisher page 8 citations

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.

About this research paper

What this paper is about

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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Available 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.

Key concepts: Cluster analysis, CURE data clustering algorithm, Canopy clustering algorithm, Correlation clustering, Hierarchical clustering, Single-linkage clustering, Fuzzy clustering, Computer science

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