A Novel Hierarchical Clustering Approach Based on Universal Gravitation

Zhang Peng; She Kun

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A Novel Hierarchical Clustering Approach Based on Universal Gravitation

机译：A Novel Hierarchical Clustering Approach Based on Universal Gravitation

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摘要

The target of the clustering analysis is to group a set of data points into several clusters based on the similarity or distance. The similarity or distance is usually a scalar used in numerous traditional clustering algorithms. Nevertheless, a vector, such as data gravitational force, contains more information than a scalar and can be applied in clustering analysis to promote clustering performance. Therefore, this paper proposes a three-stage hierarchical clustering approach called GHC, which takes advantage of the vector characteristic of data gravitational force inspired by the law of universal gravitation. In the first stage, a sparse gravitational graph is constructed based on the top k data gravitations between each data point and its neighbors in the local region. Then the sparse graph is partitioned into many subgraphs by the gravitational influence coefficient. In the last stage, the satisfactory clustering result is obtained by merging these subgraphs iteratively by using a new linkage criterion. To demonstrate the performance of GHC algorithm, the experiments on synthetic and real-world data sets are conducted, and the results show that the GHC algorithm achieves better performance than the other existing clustering algorithms.

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《Mathematical Problems in Engineering: Theory, Methods and Applications》 |2020年第3期|6748056.1-6748056.15|共15页
作者
Zhang Peng; She Kun;
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作者单位

Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 610054, Peoples R China|Southwest Univ Sci & Technol, Sch Sci, Mianyang 621010, Sichuan, Peoples R China;

Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 610054, Peoples R China;

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A Novel Hierarchical Clustering Approach Based on Universal Gravitation

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