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Gaussian-kernel c-means Clustering Algorithms

  • Miin-Shen Yang
  • Shou-Jen Chang-Chien
  • Yessica Nataliani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11324)

Abstract

K-means (or called hard c-means, HCM) and fuzzy c-means (FCM) are the most known clustering algorithms. However, the HCM and FCM algorithms work worse for the data set with different shape clusters in noisy environments. For solving these drawbacks in HCM and FCM, Wu and Yang (2002) proposed alternative c-means clustering that extends HCM and FCM into alternative HCM (AHCM) and alternative FCM (AFCM). In this paper, we further extend AHCM and AFCM as Gaussian-kernel c-means clustering, called GK-HCM and GK-FCM. Some numerical and real data sets are used to compare the proposed GK-HCM and GK-FCM with AHCM and AFCM methods. Experimental results and comparisons actually demonstrate these good aspects of the proposed GK-HCM and GK-FCM algorithms with its effectiveness and usefulness in practice.

Keywords

Clustering Hard c-means (HCM) Fuzzy c-means (FCM) Gaussian-kernel HCM (GK-HCM) Gaussian-kernel FCM (GK-FCM) 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Miin-Shen Yang
    • 1
  • Shou-Jen Chang-Chien
    • 1
  • Yessica Nataliani
    • 1
  1. 1.Department of Applied MathematicsChung Yuan Christian UniversityChung-LiTaiwan

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