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A New Clustering Algorithm Based on Graph Connectivity

  • Yu-Feng Li
  • Liang-Hung Lu
  • Ying-Chao HungEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 858)

Abstract

A new clustering algorithm based on the concept of graph connectivity is introduced. The idea is to develop a meaningful graph representation for data, where each resulting sub-graph corresponds to a cluster with highly similar objects connected by edge. The proposed algorithm has a fairly strong theoretical basis that supports its originality and computational efficiency. Further, some useful guidelines are provided so that the algorithm can be tuned to optimize the well-designed quality indices. Numerical evidences show that the proposed algorithm can provide a very good clustering accuracy for a number of benchmark data and has a relatively low computational complexity compared to some sophisticated clustering methods.

Keywords

Clustering Graph theory Time complexity 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Electrical EngineeringNational Taiwan UniversityTaipeiTaiwan
  2. 2.Department of StatisticsNational Chengchi UniversityTaipeiTaiwan

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