DocumentCode :
3165065
Title :
Weighted-Object Ensemble Clustering
Author :
Yazhou Ren ; Domeniconi, Carlotta ; Guoji Zhang ; Guoxian Yu
Author_Institution :
Sch. of Comput. Sci. & Eng., South China Univ. of Technol., Guangzhou, China
fYear :
2013
fDate :
7-10 Dec. 2013
Firstpage :
627
Lastpage :
636
Abstract :
Ensemble clustering, also known as consensus clustering, aims to generate a stable and robust clustering through the consolidation of multiple base clusterings. In recent years many ensemble clustering methods have been proposed, most of which treat each clustering and each object as equally important. Some approaches make use of weights associated with clusters, or with clusterings, when assembling the different base clusterings. Boosting algorithms developed for classification have also led to the idea of considering weighted objects during the clustering process. However, not much effort has been put towards incorporating weighted objects into the consensus process. To fill this gap, in this paper we propose an approach called Weighted-Object Ensemble Clustering (WOEC). We first estimate how difficult it is to cluster an object by constructing the co-association matrix that summarizes the base clustering results, and we then embed the corresponding information as weights associated to objects. We propose three different consensus techniques to leverage the weighted objects. All three reduce the ensemble clustering problem to a graph partitioning one. We present extensive experimental results which demonstrate that our WOEC approach outperforms state-of-the-art consensus clustering methods and is robust to parameter settings.
Keywords :
data mining; matrix algebra; pattern clustering; WOEC; coassociation matrix; consensus clustering; graph partitioning; weighted-object ensemble clustering; Bipartite graph; Boosting; Clustering algorithms; Clustering methods; Educational institutions; Partitioning algorithms; Vectors; Ensemble clustering; consensus clustering; graph partition; weighted objects;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining (ICDM), 2013 IEEE 13th International Conference on
Conference_Location :
Dallas, TX
ISSN :
1550-4786
Type :
conf
DOI :
10.1109/ICDM.2013.80
Filename :
6729547
Link To Document :
بازگشت