On Reducing Space Complexity of Fuzzy Neighborhood Based Clustering Algorithms


ATILGAN C., Nasibov E.

2017 International Conference on Computer Science and Engineering (UBMK), Antalya, Turkey, 5 - 08 October 2017, pp.577-579 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/ubmk.2017.8093467
  • City: Antalya
  • Country: Turkey
  • Page Numbers: pp.577-579
  • Keywords: lustering, Fuzzy neighborhood, Fuzzy joint points
  • Dokuz Eylül University Affiliated: Yes

Abstract

Using fuzzy neighborhood relations in density-based clustering, like in Fuzzy Joint Points (FJP) algorithm, yields more robust and autonomous algorithms. 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 transformed HP algorithm with low space complexity is proposed.