MaxMin Linear Initialization for Fuzzy C-Means

  • Aybüke ÖztürkEmail author
  • Stéphane Lallich
  • Jérôme Darmont
  • Sylvie Yona Waksman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10934)


Clustering is an extensive research area in data science. The aim of clustering is to discover groups and to identify interesting patterns in datasets. Crisp (hard) clustering considers that each data point belongs to one and only one cluster. However, it is inadequate as some data points may belong to several clusters, as is the case in text categorization. Thus, we need more flexible clustering. Fuzzy clustering methods, where each data point can belong to several clusters, are an interesting alternative. Yet, seeding iterative fuzzy algorithms to achieve high quality clustering is an issue. In this paper, we propose a new linear and efficient initialization algorithm MaxMin Linear to deal with this problem. Then, we validate our theoretical results through extensive experiments on a variety of numerical real-world and artificial datasets. We also test several validity indices, including a new validity index that we propose, Transformed Standardized Fuzzy Difference (TSFD).


Clustering Fuzzy C-Means Seeding Initialization Maxmin linear method Validity indices 



This project is supported by the Rhône Alpes Region’s ARC 5: “Cultures, Sciences, Sociétés et Médiations” through A. Öztürk’s Ph.D. grant.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Aybüke Öztürk
    • 1
    • 2
    Email author
  • Stéphane Lallich
    • 1
  • Jérôme Darmont
    • 1
  • Sylvie Yona Waksman
    • 2
  1. 1.ERIC EA 3083, Université de Lyon, Lyon 2Bron CedexFrance
  2. 2.ArAr UMR 5138, Université de Lyon, Lyon 2Lyon Cedex 7France

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