scalable clustering of categorical data

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scalable clustering of categorical data (2004)

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by Periklis Andritsos , Panayiotis Tsaparas , Renée J. Miller , Kenneth C. Sevcik

Venue:	In EDBT
Citations:	29 - 4 self

BibTeX

@INPROCEEDINGS{Andritsos04scalableclustering,
    author = {Periklis Andritsos and Panayiotis Tsaparas and Renée J. Miller and Kenneth C. Sevcik},
    title = {scalable clustering of categorical data},
    booktitle = {In EDBT},
    year = {2004},
    pages = {123--146}
}

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Abstract

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 categorical clustering algorithms. LIMBO supports a trade-off between efficiency (in terms of space and time) and quality. We quantify this trade-off and demonstrate that LIMBO allows for substantial improvements in efficiency with negligible decrease in quality. 1

Keyphrases

scalable clustering categorical data great practical importance novel distance measure experimental evaluation relevant information substantial improvement large data set hierarchical algorithm ib framework data value different size summary model numerous application limbo algorithm clustering quality compare distance measure categorical tuples inherent distance measure single execution negligible decrease scalable hierarchical categorical clustering algorithm information bottleneck categorical attribute value

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