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A neighborhood-based three-stage hierarchical clustering algorithm

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Abstract

Many neighborhood-based clustering algorithms have been proposed to measure the similarity between data points or subclusters with their neighborhood information. However, most of them are vulnerable to the different cluster sizes, shapes and densities. In this paper, we propose a neighborhood-based three-stage hierarchical clustering algorithm (NTHC) which is robust to the difference. Three concepts, i.e., the stability of data point pair, the linked representatives, and the expanded representatives, are defined. Furthermore, a new measure of intercluster distance based on representatives is designed. In Stage 1, the outliers are detected and removed from the data set using reverse nearest neighbors. In Stage 2, small clusters are formed by merging the data points with stable connection on 1-nearest neighbor graph. In Stage 3, the final partitions are obtained by iteratively merging the closest pair of clusters based on the new measure of intercluster distance. Tests are carried out to compare the proposal with 15 other clustering algorithms. The experimental results on synthetic and real data sets demonstrate the proposed method is effective. In addition, we test the statistically significant differences among the sixteen clustering algorithms using the Friedman test. And the average rank value of the proposed algorithm is 4.19, which is superior to the other algorithms.

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  1. https://github.com/yian158/clustering-analysis.

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Acknowledgments

This work is partially supported by the National Natural Science Foundation of China (Grant no. 61373004, 61501297).

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  1. College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China

    Yan Wang, Yan Ma & Hui Huang

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  1. Yan Wang
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Correspondence to Yan Ma.

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Wang, Y., Ma, Y. & Huang, H. A neighborhood-based three-stage hierarchical clustering algorithm. Multimed Tools Appl 80, 32379–32407 (2021). https://doi.org/10.1007/s11042-021-11171-w

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