Clustering Similarity Comparison Using Density Profiles

The unsupervised nature of cluster analysis means that objects can be clustered in many different ways. This means that different clustering algorithms can lead to vastly different results. To address this, clustering similarity comparison methods have traditionally been used to quantify the degree of similarity between alternative clusterings. However, existing techniques utilize only the point-to-cluster memberships to calculate the similarity, which can lead to unintuitive results. They also can't be applied to analyze clusterings which only partially share points, which can be the case in stream clustering. In this paper we introduce a new measure named ADCO, which takes into account density profiles for each attribute and aims to address these problems. We provide experiments to demonstrate this new measure can often provide a more reasonable similarity comparison between different clusterings than existing methods.

• Type: Conference
• Year: 2006
• Where: AUSAI
• Authors: Eric Bae, James Bailey, Guozhu Dong
• Keywords: Artificial Intelligence, AUSAI 2006, Clustering Similarity Comparison, Reasonable Similarity Comparison, Similarity Comparison