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arXiv · 0711.3594

Clustering with Transitive Distance and K-Means Duality

Chunjing Xu·Jianzhuang Liu·Xiaoou Tang
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Abstract

Recent spectral clustering methods are a propular and powerful technique for data clustering. These methods need to solve the eigenproblem whose computational complexity is $O(n^3)$, where $n$ is the number of data samples. In this paper, a non-eigenproblem based clustering method is proposed to deal with the clustering problem. Its performance is comparable to the spectral clustering algorithms but it is more efficient with computational complexity $O(n^2)$. We show that with a transitive distance and an observed property, called K-means duality, our algorithm can be used to handle data sets with complex cluster shapes, multi-scale clusters, and noise. Moreover, no parameters except the number of clusters need to be set in our algorithm.

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Chunjing Xu, Jianzhuang Liu, Xiaoou Tang. 2007-11-22. Clustering with Transitive Distance and K-Means Duality. https://arxiv.org/abs/0711.3594

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