A Theoretic Framework of K-Means-Based Consensus Clustering

Wu, Junjie; Liu, Hongfu; Xiong, Hui; Cao, Jie

A Theoretic Framework of K-Means-Based Consensus Clustering

Junjie Wu, Hongfu Liu, Hui Xiong, Jie Cao

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Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 1799-1804.

Abstract

Consensus clustering emerges as a promising solution to find cluster structures from data. As an efficient approach for consensus clustering, the K-means based method has garnered attention in the literature, but the existing research is still preliminary and fragmented. In this paper, we provide a systematic study on the framework of K-means-based Consensus Clustering (KCC). We first formulate the general definition of KCC, and then reveal a necessary and sufficient condition for utility functions that work for KCC, on both complete and incomplete basic partitionings. Experimental results on various real-world 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 substantial missing values.

Junjie Wu, Hongfu Liu, Hui Xiong, and Jie Cao. 2013. A Theoretic Framework of K-Means-Based Consensus Clustering. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13. IJCAI, 1799–1804. https://ijcai.org/proceedings/2013/266

@inproceedings{ijcai2013p266,
  title     = {A Theoretic Framework of K-Means-Based Consensus Clustering},
  author    = {Wu, Junjie and Liu, Hongfu and Xiong, Hui and Cao, Jie},
  booktitle = {Proceedings of the Twenty-Third International Joint Conference on
               Artificial Intelligence, {IJCAI-13}},
  publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
  editor    = {Francesca Rossi},
  pages     = {1799--1804},
  year      = {2013},
  month     = {8},
  url       = {https://ijcai.org/proceedings/2013/266},
}

TY  - CPAPER
TI  - A Theoretic Framework of K-Means-Based Consensus Clustering
AU  - Wu, Junjie
AU  - Liu, Hongfu
AU  - Xiong, Hui
AU  - Cao, Jie
T2  - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13
PB  - AAAI Press / International Joint Conferences on Artificial Intelligence
PY  - 2013
SP  - 1799
EP  - 1804
UR  - https://ijcai.org/proceedings/2013/266
ER  -