A Neural Gas Based Approximate Spectral Clustering Ensemble


Moazzen Y., TAŞDEMİR K.

11th International Workshop on Self-Organizing Maps (WSOM), Texas, United States Of America, 6 - 08 January 2016, vol.428, pp.85-93 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 428
  • Doi Number: 10.1007/978-3-319-28518-4_7
  • City: Texas
  • Country: United States Of America
  • Page Numbers: pp.85-93

Abstract

The neural gas has been successfully used for prototype based clustering approaches. Its topology based quantization effectively aids in approximate spectral clustering (ASC) to define distinct similarity criteria which are optimally selected for the relevant application. To utilize the advantages of ASC by harnessing those criteria derived from different information types, we propose a neural gas based approximate spectral clustering ensemble (NGASCE). The NGASCE obtains a joint decision for accurate partitioning, by a 2-step ensemble approach derived from 1-step graph-based models. We show the outperformance of NGASCE on five datasets from UCI Machine Learning Repository.