LIMBO: Scalable Clustering of Categorical Data

Abstract. Clustering is a problem of great practical importance in numerous applications. The problem of clustering becomes more challenging when the data is categorical, that is, when there is no inherent distance measure between data values. We introduce LIMBO, a scalable hierarchical categorical clustering algorithm that builds on the Information Bottleneck (IB) framework for quantifying the relevant information preserved when clustering. As a hierarchical algorithm, LIMBO has the advantage that it can produce clusterings of different sizes in a single execution. We use the IB framework to define a distance measure for categorical tuples and we also present a novel distance measure for categorical attribute values. We show how the LIMBO algorithm can be used to cluster both tuples and values. LIMBO handles large data sets by producing a memory bounded summary model for the data. We present an experimental evaluation of LIMBO, and we study how clustering quality compares to other cat...

• Type: Conference
• Year: 2004
• Where: EDBT
• Authors: Periklis Andritsos, Panayiotis Tsaparas, Renée J. Miller, Kenneth C. Sevcik
• Keywords: Categorical Clustering Algorithm, Categorical Clustering Algorithms, Database, EDBT 2004, LIMBO Algorithm