|
|
|
Date Submitted:
07/11/06
Hits: 36 Rating:  based on 0 votes
Scalable Clustering Algorithms with Balancing ConstraintsAdded by Papergrl
Description:
In this paper, we propose a general framework for scalable, balanced clustering. The data clustering process is broken down into three steps: sampling of a small representative subset of the points, clustering of the sampled data, and populating the initial clusters with the remaining data followed by refinements. First, we show that a simple uniform sampling from the original data is sufficient to get a representative subset with high probability. While the proposed framework allows a large class of algorithms to be used for clustering the sampled set, we focus on some popular parametric algorithms for ease of exposition. We then present algorithms to populate and refine the clusters.
Read the Complete Paper You don't have permission to post replies. Please login or register. |