Semi-supervised hierarchical clustering ensemble and its applications

Impact Factor:3.317

DOI number:10.1016/j.neucom.2015.09.009

Affiliation of Author(s):Southwest Jiaotong Univ，Sch Informat Sci & Technol

Journal:Neurocomputing

Key Words:Clustering ensemble,Semi-supervised,CHAMELEON,Fault diagnosis

Abstract:Clustering ensemble is an important part of ensemble learning. It aims to study and integrate multiple clustering results from different clustering algorithms or same algorithm with different initial parameters for the same dataset. CHAMELEON is a hierarchical clustering algorithm which can discover natural clusters of different shapes and sizes as the result of its merging decision dynamically adapts to the different clustering model characterized. Inspired by the idea of CHAMELEON, the paper proposes a novel clustering ensemble models including semi-supervised method and discusses its application in fault diagnosis of high speed train (HST) running gear. The contributions of this paper include: constructing a sparse graph via the similarity matrix which aggregates multiple clustering results; partitioning the sparse graph (vertex = object, edge weight = similarity) into a large number of relatively small subclusters; obtaining the final clustering partition by merging these sub-clusters repeatedly. The experimental results demonstrate that our method outperforms some of state-of-the-art ensemble algorithms regarding the accuracy and stability and recognizes fault patterns of HST running gear effectively. (C) 2015 Elsevier B.V. All rights reserved.

Co-author:Wenchao Xiao,Yan Yang,Hongjun Wang,Tianrui Li,Huanlai Xing

Document Code:10.1016/j.neucom.2015.09.009

Volume:173

Page Number:1362-1376

ISSN No.:0925-2312

Translation or Not:no

Date of Publication:2016-01-15

Included Journals:SCI