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.
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References
Andritsos, P., Tsaparas, P., Miller, R.J., Sevcik, K.C.: Limbo: A linear algorithm to cluster categorical data. Technical report, UofT, Dept of CS, CSRG-467 (2003)
Andritsos, P., Tzerpos, V.: Software Clustering based on Information Loss Minimization. In: WCRE, Victoria, BC, Canada (2003)
Barbará, D., Couto, J., Li, Y.: An Information Theory Approach to Categorical Clustering (submitted for Publication)
Barbará, D., Couto, J., Li, Y.: COOLCAT: An entropy-based algorithm for categorical clustering. In: CIKM, McLean, VA (2002)
Borodin, A., Roberts, G.O., Rosenthal, J.S., Tsaparas, P.: Finding authorities and hubs from link structures on the World Wide Web. In: WWW-10, Hong Kong (2001)
Chiu, T., Fang, D., Chen, J., Wang, Y., Jeris, C.: A Robust and Scalable Clustering Algorithm for Mixed Type Attributes in Large Database Environment. In: KDD, San Francisco, CA (2001)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley & Sons, Chichester (1991)
Barbará, D.: Requirements for Clustering Data Streams. SIGKDD Explorations 3(2) (January 2002)
Das, G., Mannila, H.: Context-Based Similarity Measures for Categorical Databases. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 201–210. Springer, Heidelberg (2000)
Ganti, V., Gehrke, J., Ramakrishnan, R.: CACTUS: Clustering Categorical Data Using Summaries. In: KDD, San Diego, CA (1999)
Garey, M.R., Johnson, D.S.: Computers and intractability; a guide to the theory of NP-completeness. W.H. Freeman, New York (1979)
Gibson, D., Kleinberg, J.M., Raghavan, P.: Clustering Categorical Data: An Approach Based on Dynamical Systems. In: VLDB, New York, NY (1998)
Guha, S., Rastogi, R., Shim, K.: ROCK: A Robust Clustering Algorithm for Categorical Atributes. In: ICDE, Sydney, Australia (1999)
Kleinberg, J.M.: Authoritative Sources in a Hyperlinked Environment. In: SODA, SF, CA (1998)
Gluck, M.A., Corter, J.E.: Information, Uncertainty, and the Utility of Categories. In: COGSCI, Irvine, CA, USA (1985)
Miller, R.J., Andritsos, P.: On Schema Discovery. IEEE Data Engineering Bulletin 26(3), 39–44 (2003)
Slonim, N., Friedman, N., Tishby, N.: Unsupervised Document Classification using Sequential Information Maximization. In: SIGIR, Tampere, Finland (2002)
Slonim, N., Tishby, N.: Agglomerative Information Bottleneck. In: NIPS, Breckenridge (1999)
Slonim, N., Tishby, N.: Document Clustering Using Word Clusters via the Information Bottleneck Method. In: SIGIR, Athens, Greece (2000)
Tishby, N., Pereira, F.C., Bialek, W.: The Information Bottleneck Method. In: 37th Annual Allerton Conference on Communication, Control and Computing, Urban-Champaign, IL (1999)
Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: An efficient Data Clustering Method for Very Large Databases. In: SIGMOD, Montreal, QB (1996)
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Andritsos, P., Tsaparas, P., Miller, R.J., Sevcik, K.C. (2004). LIMBO: Scalable Clustering of Categorical Data. In: Bertino, E., et al. Advances in Database Technology - EDBT 2004. EDBT 2004. Lecture Notes in Computer Science, vol 2992. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24741-8_9
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DOI: https://doi.org/10.1007/978-3-540-24741-8_9
Publisher Name: Springer, Berlin, Heidelberg
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