A Space Efficient Minimum Spanning Tree Approach to the Fuzzy Joint Points Clustering Algorithm

ATILGAN C., Nasibov E.

IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol.27, no.6, pp.1317-1322, 2019 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 27 Issue: 6
  • Publication Date: 2019
  • Doi Number: 10.1109/tfuzz.2018.2879465
  • Journal Name: IEEE TRANSACTIONS ON FUZZY SYSTEMS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1317-1322
  • Keywords: Clustering, fuzzy joint points (FJPs), fuzzy neighborhood, space efficiency
  • Dokuz Eylül University Affiliated: Yes

Abstract

The fuzzy joint points (FJPs) method is a neighborhood-based clustering method that uses a fuzzy neighborhood relation and eliminates the need for a parameter. Even though the fuzzy neighborhood-based clustering methods are proven to be fast enough, such that tens of thousands of data can be handled under a second, the space complexity is still a limiting factor. In this study, a minimum spanning tree based reduced space FJP algorithm is proposed. The computational experiments show that the reduced space algorithm enables the method to be used for much larger data sets.