Cluster Forests Based Fuzzy C-Means for Data Clustering

• Abdelkarim Ben Ayed ^[1] ; Mohamed Ben Halima ^[1] ; Alimi, Adel M. ^[1]
  1. [1] University of Sfax

     University of Sfax

     Túnez

     
• Localización: International Joint Conference SOCO’16-CISIS’16-ICEUTE’16: San Sebastián, Spain, October 19th-21st, 2016 Proceedings / coord. por Manuel Graña Romay , José Manuel López Guede , Oier Echaniz, Álvaro Herrero Cosío , Héctor Quintián Pardo , Emilio Santiago Corchado Rodríguez , 2017, ISBN 978-3-319-47364-2, págs. 564-573
• Idioma: inglés
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• Resumen

  • Cluster forests is a novel approach for ensemble clustering based on the aggregation of partial K-means clustering trees. Cluster forests was inspired from random forests algorithm. Cluster forests gives better results than other popular clustering algorithms on most standard benchmarks. In this paper, we propose an improved version of cluster forests using fuzzy C-means clustering. Results shows that the proposed Fuzzy Cluster Forests system gives better clustering results than cluster forests for eight standard clustering benchmarks from UC Irvine Machine Learning Repository.