Criterion Functions for Document Clustering ∗ Experiments and Analysis

In recent years, we have witnessed a tremendous growth in the volume of text documents available on the Internet, digital libraries, news sources, and company-wide intranets. This has led to an increased interest in developing methods that can help users to effectively navigate, summarize, and organize this information with the ultimate goal of helping them to find what they are looking for. Fast and high-quality document clustering algorithms play an important role towards this goal as they have been shown to provide both an intuitive navigation/browsing mechanism by organizing large amounts of information into a small number of meaningful clusters as well as to greatly improve the retrieval performance either via cluster-driven dimensionality reduction, term-weighting, or query expansion. This ever-increasing importance of document clustering and the expanded range of its applications led to the development of a number of new and novel algorithms with different complexity-quality trade-offs. Among them, a class of clustering algorithms that have relatively low computational requirements are those that treat the clustering problem as an optimization process which seeks to maximize or minimize a particular clustering criterion function defined over the entire clustering solution. The focus of this paper is to evaluate the performance of different criterion functions for the problem of clustering documents. Our study involves a total of seven different criterion functions, three of which are introduced in this paper and four that have been proposed in the past. Our evaluation consists of both a comprehensive experimental evaluation involving fifteen different datasets, as well as an analysis of the characteristics of the various criterion functions and their effect on the clusters they produce. Our experimental results show that there are a set of criterion functions that consistently outperform the rest, and that some of the newly proposed criterion functions lead to the best overall results. Our theoretical analysis of the criterion function shows that their relative performance depends on (i) the degree to which they can correctly operate when the clusters are of different tightness, and (ii) the degree to which they can lead to reasonably balanced clusters.

[1]  P. Sneath,et al.  Numerical Taxonomy , 1962, Nature.

[2]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[3]  Benjamin King.  Step-Wise Clustering Procedures , 1967 .

[4]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[5]  Charles T. Zahn,et al.  Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters , 1971, IEEE Transactions on Computers.

[6]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[7]  Martin F. Porter,et al.  An algorithm for suffix stripping , 1997, Program.

[8]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[9]  Gerard Salton,et al.  Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer , 1989 .

[10]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[11]  David R. Karger,et al.  Scatter/Gather: a cluster-based approach to browsing large document collections , 1992, SIGIR '92.

[12]  J. Edward Jackson,et al.  A User's Guide to Principal Components. , 1991 .

[13]  Chris Buckley,et al.  OHSUMED: an interactive retrieval evaluation and new large test collection for research , 1994, SIGIR '94.

[14]  Susan T. Dumais,et al.  Using Linear Algebra for Intelligent Information Retrieval , 1995, SIAM Rev..

[15]  Tian Zhang,et al.  BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.

[16]  Peter C. Cheeseman,et al.  Bayesian Classification (AutoClass): Theory and Results , 1996, Advances in Knowledge Discovery and Data Mining.

[17]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[18]  Kaizhong Zhang,et al.  Automated Discovery of Active Motifs in Three Dimensional Molecules , 1997, KDD.

[19]  David D. Lewis,et al.  Reuters-21578 Text Categorization Test Collection, Distribution 1.0 , 1997 .

[20]  Robert H. Gross,et al.  Web Page Categorization and Feature Selection Using Association Rule and Principal Component Cluster , 1997 .

[21]  Sudipto Guha,et al.  CURE: an efficient clustering algorithm for large databases , 1998, SIGMOD '98.

[22]  Vipin Kumar,et al.  Hypergraph Based Clustering in High-Dimensional Data Sets: A Summary of Results , 1998, IEEE Data Eng. Bull..

[23]  Vipin Kumar,et al.  WebACE: a Web agent for document categorization and exploration , 1998, AGENTS '98.

[24]  Vipin Kumar,et al.  A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs , 1998, SIAM J. Sci. Comput..

[25]  George Karypis,et al.  C HAMELEON : A Hierarchical Clustering Algorithm Using Dynamic Modeling , 1999 .

[26]  G. Karypis,et al.  Multilevel Refinement for Hierarchical Clustering , 1999 .

[27]  Vipin Kumar,et al.  Partitioning-based clustering for Web document categorization , 1999, Decis. Support Syst..

[28]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[29]  Sudipto Guha,et al.  ROCK: a robust clustering algorithm for categorical attributes , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[30]  Philip S. Yu,et al.  On the merits of building categorization systems by supervised clustering , 1999, KDD '99.

[31]  Chinatsu Aone,et al.  Fast and effective text mining using linear-time document clustering , 1999, KDD '99.

[32]  Joydeep Ghosh,et al.  A S alable Approa h to Balan ed, High-dimensional Clustering of Market-baskets , 2000 .

[33]  George Karypis,et al.  A Comparison of Document Clustering Techniques , 2000 .

[34]  George Karypis,et al.  Concept Indexing: A Fast Dimensionality Reduction Algorithm With Applications to Document Retrieval and Categorization , 2000 .

[35]  Chris H. Q. Ding,et al.  Spectral Relaxation for K-means Clustering , 2001, NIPS.

[36]  Chris H. Q. Ding,et al.  Bipartite graph partitioning and data clustering , 2001, CIKM '01.

[37]  Anthony K. H. Tung,et al.  Spatial clustering methods in data mining : A survey , 2001 .

[38]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[39]  Bipartite graph partitioning and data clustering , 2001 .

[40]  Inderjit S. Dhillon,et al.  Co-clustering documents and words using bipartite spectral graph partitioning , 2001, KDD '01.

[41]  Ming Gu,et al.  Spectral min-max cut for graph partitioning and data clustering , 2001 .