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Semantic feature hierarchical clustering algorithm based on improved regional merging strategy

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Abstract

Hierarchical clustering based on semantic feature thresholds is well applied in data mining. However, this method uses systematic sampling to select the edge points of the region during the region merging phase, resulting in the problems of under-consolidation and computational complexity when region merging based on the Euclidean distance criterion. To solve this problem, an improved hierarchical clustering method is proposed, which adopts the weighted Euclidean distance and optimizes the components of region combination. According to the orientation of the centers of two region clustering and the geometric relation among the edge points, two novel type of edge point selection strategies are established to reduce the number of edge points involved in the operation and ensure that the selected ones possess the minimum Euclidean distance in all edge point combinations. Experimental results show that the main advantages of this research project lie in its high clustering accuracy and less average clustering time.

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Authors and Affiliations

  1. The Institute of Intelligent Information Processing and Application, Soochow University, Suzhou, 215006, China

    Yao yu-feng

  2. School of Computing Science and Engineering, Changshu Institute of Technology, Changshu, 215500, China

    Yao yu-feng

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  1. Yao yu-feng
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Correspondence to Yao yu-feng.

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yu-feng, Y. Semantic feature hierarchical clustering algorithm based on improved regional merging strategy. Cluster Comput 22 (Suppl 1), 1495–1503 (2019). https://doi.org/10.1007/s10586-018-1941-5

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  • DOI: https://doi.org/10.1007/s10586-018-1941-5

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