Improving clustering stability with combinatorial MRFs

: ? Improving Clustering Stability with Combinatorial MRFs Bekkerman, Ron; Scholz, Martin; Viswanathan, Krishnamurthy HP Laboratories HPL-2009-46 Clustering stability, combinatorial MRF, Comraf As clustering methods are often sensitive to parameter tuning, obtaining stability in clustering results is an important task. In this work, we aim at improving clustering stability by attempting to diminish the influence of algorithmic inconsistencies and enhance the signal that comes from the data. We propose a mechanism that takes m clusterings as input and outputs m clusterings of comparable quality, which are in higher agreement with each other. We call our method the Clustering Agreement Process (CAP). To preserve the clustering quality, CAP uses the same optimization procedure as used in clustering. In particular, we study the stability problem of randomized clustering methods (which usually produce different results at each run). We focus on methods that are based on inference in a combi...

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: Ron Bekkerman, Martin Scholz, Krishnamurthy Viswanathan
• Keywords: Clustering Quality, Clustering Solutions, Clustering Stability, Data Mining, KDD 2009