K-Means-Based Consensus Clustering: A Unified View

The objective of consensus clustering is to find a single partitioning which agrees as much as possible with existing basic partitionings. Consensus clustering emerges as a promising solution to find cluster structures from heterogeneous data. As an efficient approach for consensus clustering, the K...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2015-01, Vol.27 (1), p.155-169
Main Authors: Wu, Junjie, Liu, Hongfu, Xiong, Hui, Cao, Jie, Chen, Jian
Format: Article
Language:English
Subjects:
Clustering algorithms
Convex functions
Educational institutions
Linear programming
Partitioning algorithms
Robustness
Vectors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The objective of consensus clustering is to find a single partitioning which agrees as much as possible with existing basic partitionings. Consensus clustering emerges as a promising solution to find cluster structures from heterogeneous data. As an efficient approach for consensus clustering, the K-means based method has garnered attention in the literature, however the existing research efforts are still preliminary and fragmented. To that end, in this paper, we provide a systematic study of K-means-based consensus clustering (KCC). Specifically, we first reveal a necessary and sufficient condition for utility functions which work for KCC. This helps to establish a unified framework for KCC on both complete and incomplete data sets. Also, we investigate some important factors, such as the quality and diversity of basic partitionings, which may affect the performances of KCC. Experimental results on various realworld data sets demonstrate that KCC is highly efficient and is comparable to the state-of-the-art methods in terms of clustering quality. In addition, KCC shows high robustness to incomplete basic partitionings with many missing values.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2014.2316512