ROCK: A Robust Clustering Algorithm for Categorical Attributes

Clustering, in data mining, is useful to discover distribution patterns in the underlying data. Clustering algorithms usually employ a distance metric based (e.g., euclidean) similarity measure in order to partition the database such that data points in the same partition are more similar than points in different partitions. In this paper, we study clustering algorithms for data with boolean and categorical attributes. We show that traditional clustering algorithms that use distances between points for clustering are not appropriate for boolean and categorical attributes. Instead, we propose a novel concept of links to measure the similarity/proximity between a pair of data points. We develop a robust hierarchical clustering algorithm ROCK that employs links and not distances when merging clusters. Our methods naturally extend to non-metric similarity measures that are relevant in situations where a domain expert/similarity table is the only source of knowledge. In addition to present...

• Type: Conference
• Year: 1999
• Where: ICDE
• Authors: Sudipto Guha, Rajeev Rastogi, Kyuseok Shim
• Keywords: Categorical Attributes, Clustering Algorithm Rock, Database, ICDE 1999, Traditional Clustering Algorithms