---
title: "A Theoretic Framework of K-Means-Based Consensus Clustering"
authors:
  - "Junjie Wu"
  - "Hongfu Liu"
  - "Hui Xiong"
  - "Jie Cao"
doi: ""
url: "https://ijcai.org/proceedings/2013/266"
pdf: "https://ijcai.org/proceedings/2013/0266.pdf"
venue: "Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence"
year: 2013
pages: "1799-1804"
track: ""
keywords:
---

# A Theoretic Framework of K-Means-Based Consensus Clustering

Junjie Wu, Hongfu Liu, Hui Xiong, Jie Cao

*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.