Clustering by Local Gravitation

Zhiqiang Wang, Zhiwen Yu, C. L. Philip Chen, Jia You, Tianlong Gu, Hau San Wong, Jun Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

21 Citations (Scopus)

Abstract

The objective of cluster analysis is to partition a set of data points into several groups based on a suitable distance measure. We first propose a model called local gravitation among data points. In this model, each data point is viewed as an object with mass, and associated with a local resultant force (LRF) generated by its neighbors. The motivation of this paper is that there exist distinct differences between the LRFs (including magnitudes and directions) of the data points close to the cluster centers and at the boundary of the clusters. To capture these differences efficiently, two new local measures named centrality and coordination are further investigated. Based on empirical observations, two new clustering methods called local gravitation clustering and communication with local agents are designed, and several test cases are conducted to verify their effectiveness. The experiments on synthetic data sets and real-world data sets indicate that both clustering approaches achieve good performance on most of the data sets.
Original languageEnglish
Pages (from-to)1383-1396
Number of pages14
JournalIEEE Transactions on Cybernetics
Volume48
Issue number5
DOIs
Publication statusPublished - 1 May 2018

Keywords

  • Cluster algorithms
  • cluster analysis
  • clustering
  • density-based clustering

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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