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

ATILGAN C., Nasibov E.

IEEE TRANSACTIONS ON FUZZY SYSTEMS, cilt.27, sa.6, ss.1317-1322, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 27 Sayı: 6
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1109/tfuzz.2018.2879465
  • Dergi Adı: IEEE TRANSACTIONS ON FUZZY SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1317-1322
  • Anahtar Kelimeler: Clustering, fuzzy joint points (FJPs), fuzzy neighborhood, space efficiency
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

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.