Clustering with Transitive Distance and K-Means Duality

Details Clustering with Transitive Distance and K-Means Duality Clustering with Transitive Distance and K-Means Duality

2007-11-22

arxiv.org/abs/0711.3594v1

Computer Science

Learning

13 pages, 6 figures

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Clustering with Transitive Distance and K-Means Duality does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Clustering with Transitive Distance and K-Means Duality, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Clustering with Transitive Distance and K-Means Duality will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-310913