Article

A hybrid unsupervised approach for document clustering

Authors:

Mihai Surdeanu,

Jordi Turmo,

Alicia AgenoAuthors Info & Claims

KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining

Pages 685 - 690

https://doi.org/10.1145/1081870.1081957

Published: 21 August 2005 Publication History

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Abstract

We propose a hybrid, unsupervised document clustering approach that combines a hierarchical clustering algorithm with Expectation Maximization. We developed several heuristics to automatically select a subset of the clusters generated by the first algorithm as the initial points of the second one. Furthermore, our initialization algorithm generates not only an initial model for the iterative refinement algorithm but also an estimate of the model dimension, thus eliminating another important element of human supervision. We have evaluated the proposed system on five real-world document collections. The results show that our approach generates clustering solutions of higher quality than both its individual components.

References

[1]

T. Calinski and J. Harabasz. A dendrite method for cluster analysis. Communications in Statistics, 3:1--27, 1974.

Google Scholar

[2]

A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39(1), 1977.

Google Scholar

[3]

R. O. Duda and P. E. Hart. Pattern Classification and Scene Analysis. John Wiley and Sons, New York, 1973.

Digital Library

Google Scholar

[4]

M. Meila and D. Heckerman. An experimental comparison of several clustering and initialization methods. In Microsoft Technical Report MSR-TR-98-06, 1998.

Google Scholar

[5]

G. W. Milligan and M. C. Cooper. An examination of procedures for determining the number of clusters in a data set. Psychometrika, 50, 1985.

Google Scholar

[6]

K. Nigam, A. McCallum, S. Thrun, and T. Mitchell. Text classification from labeled and unlabeled documents using EM. Machine Learning, 39(2/3), 2000.

Digital Library

Google Scholar

[7]

E. Riloff. Automatically generating extraction patterns from untagged text. In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), 1996.

Digital Library

Google Scholar

[8]

T. G. Rose, M. Stevenson, and M. Whitehead. The Reuters corpus volume 1 - from yesterday's news to tomorrow's language resources. In Proceedings of the Third International Conference on Language Resources and Evaluation, 2002.

Google Scholar

[9]

G. Schwartz. Estimating the dimension of a model. The Annals of Statistics, 5(2), 1978.

Google Scholar

[10]

Y. Zhao and G. Karypis. Evaluation of hierarchical clustering algorithms for document datasets. In Proceedings of the International Conference on Information and Knowledge Management, 2002.

Digital Library

Google Scholar

[11]

Y. Zhao and G. Karypis. Empirical and theoretical comparisons of selected criterion functions for document clustering. Machine Learning, 55(3), 2004.

Digital Library

Google Scholar

Cited By

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Yıldırım AÖzdoğan CWatson D(2016)Parallel Data Reduction Techniques for Big DatasetsBig Data10.4018/978-1-4666-9840-6.ch034(734-756)Online publication date: 2016
https://doi.org/10.4018/978-1-4666-9840-6.ch034
Jin CChen ZHendrix WAgrawal AChoudhary AWatson LWeinbub JSosonkina MThacker W(2015)Incremental, distributed single-linkage hierarchical clustering algorithm using mapreduceProceedings of the Symposium on High Performance Computing10.5555/2872599.2872610(83-92)Online publication date: 12-Apr-2015
https://dl.acm.org/doi/10.5555/2872599.2872610
Yıldırım AÖzdoğan CWatson D(2014)Parallel Data Reduction Techniques for Big DatasetsBig Data Management, Technologies, and Applications10.4018/978-1-4666-4699-5.ch004(72-93)Online publication date: 2014
https://doi.org/10.4018/978-1-4666-4699-5.ch004
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Index Terms

A hybrid unsupervised approach for document clustering
1. Information systems
  1. Information retrieval

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Published In

KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining

August 2005

844 pages

ISBN:159593135X

DOI:10.1145/1081870

General Chair:
Robert Grossman
University of Illinois at Chicago & Open Data Partners, USA
,
Program Chairs:
Roberto Bayardo
IBM Almaden Research, USA
,
Kristin Bennett
RPI, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 August 2005

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KDD05

Sponsor:

KDD05: The Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 21 - 24, 2005

Illinois, Chicago, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Yıldırım AÖzdoğan CWatson D(2016)Parallel Data Reduction Techniques for Big DatasetsBig Data10.4018/978-1-4666-9840-6.ch034(734-756)Online publication date: 2016
https://doi.org/10.4018/978-1-4666-9840-6.ch034
Jin CChen ZHendrix WAgrawal AChoudhary AWatson LWeinbub JSosonkina MThacker W(2015)Incremental, distributed single-linkage hierarchical clustering algorithm using mapreduceProceedings of the Symposium on High Performance Computing10.5555/2872599.2872610(83-92)Online publication date: 12-Apr-2015
https://dl.acm.org/doi/10.5555/2872599.2872610
Yıldırım AÖzdoğan CWatson D(2014)Parallel Data Reduction Techniques for Big DatasetsBig Data Management, Technologies, and Applications10.4018/978-1-4666-4699-5.ch004(72-93)Online publication date: 2014
https://doi.org/10.4018/978-1-4666-4699-5.ch004
Patwary MSatish NSundaram NManne FHabib SDubey PDamkroger TDongarra J(2014)PardicleProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1109/SC.2014.51(560-571)Online publication date: 16-Nov-2014
https://dl.acm.org/doi/10.1109/SC.2014.51
Patwary MPalsetia DAgrawal ALiao WManne FChoudhary AGropp WMatsuoka S(2013)Scalable parallel OPTICS data clustering using graph algorithmic techniquesProceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis10.1145/2503210.2503255(1-12)Online publication date: 17-Nov-2013
https://dl.acm.org/doi/10.1145/2503210.2503255
Chen HRoco MSon JJiang SLarson CGao Q(2013)Global nanotechnology development from 1991 to 2012: patents, scientific publications, and effect of NSF fundingJournal of Nanoparticle Research10.1007/s11051-013-1951-415:9Online publication date: 30-Aug-2013
https://doi.org/10.1007/s11051-013-1951-4
Lee HRecasens MChang ASurdeanu MJurafsky DTsujii JHenderson JPasca M(2012)Joint entity and event coreference resolution across documentsProceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning10.5555/2390948.2391006(489-500)Online publication date: 12-Jul-2012
https://dl.acm.org/doi/10.5555/2390948.2391006
Patwary MPalsetia DAgrawal ALiao WManne FChoudhary AHollingsworth J(2012)A new scalable parallel DBSCAN algorithm using the disjoint-set data structureProceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis10.5555/2388996.2389081(1-11)Online publication date: 10-Nov-2012
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Pawling AYan PCandia JSchoenharl TMadey G(2012)Anomaly Detection in Streaming Sensor DataWireless Technologies10.4018/978-1-61350-101-6.ch403(910-928)Online publication date: 2012
https://doi.org/10.4018/978-1-61350-101-6.ch403
Azzopardi JStaff C(2012)Incremental Clustering of News ReportsAlgorithms10.3390/a50303645:3(364-378)Online publication date: 24-Aug-2012
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