Learning spatially variant dissimilarity (SVaD) measures

Clustering algorithms typically operate on a feature vector representation of the data and find clusters that are compact with respect to an assumed (dis)similarity measure between the data points in feature space. This makes the type of clusters identified highly dependent on the assumed similarity measure. Building on recent work in this area, we formally define a class of spatially varying dissimilarity measures and propose algorithms to learn the dissimilarity measure automatically from the data. The idea is to identify clusters that are compact with respect to the unknown spatially varying dissimilarity measure. Our experiments show that the proposed algorithms are more stable and achieve better accuracy on various textual data sets when compared with similar algorithms proposed in the literature. Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications-Data Mining General Terms Algorithms Keywords Clustering, Learning Dissimilarity Measures

• Type: Conference
• Year: 2004
• Where: KDD
• Authors: Krishna Kummamuru, Raghu Krishnapuram, Rakesh Agrawal
• Keywords: Assumed Similarity Measure, Data Mining, KDD 2004, Similar Algorithms, Terms Algorithms Keywords