research-article

Mixture models for learning low-dimensional roles in high-dimensional data

Authors:

Christopher Jermaine,

Sanjay RankaAuthors Info & Claims

KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 909 - 918

https://doi.org/10.1145/1835804.1835919

Published: 25 July 2010 Publication History

Abstract

Archived data often describe entities that participate in multiple roles. Each of these roles may influence various aspects of the data. For example, a register transaction collected at a retail store may have been initiated by a person who is a woman, a mother, an avid reader, and an action movie fan. Each of these roles can influence various aspects of the customer's purchase: the fact that the customer is a mother may greatly influence the purchase of a toddler-sized pair of pants, but have no influence on the purchase of an action-adventure novel. The fact that the customer is an action move fan and an avid reader may influence the purchase of the novel, but will have no effect on the purchase of a shirt.

In this paper, we present a generic, Bayesian framework for capturing exactly this situation. In our framework, it is assumed that multiple roles exist, and each data point corresponds to an entity (such as a retail customer, or an email, or a news article) that selects various roles which compete to influence the various attributes associated with the data point. We develop robust, MCMC algorithms for learning the models under the framework.

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References

[1]

C. C. Aggarwal, J. L. Wolf, P. S. Yu, C. Procopiuc, and J. S. Park. Fast algorithms for projected clustering. In SIGMOD, pages 61--72, New York, NY, USA, 1999. ACM Press.

Digital Library

[2]

R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. In SIGMOD, pages 94--105, New York, NY, USA, 1998. ACM Press.

Digital Library

[3]

A. Asuncion and D. Newman. UCI machine learning repository. http://www.ics.uci.edu/~mlearn/MLRepository.html, 2007.

[4]

D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993--1022, 2003.

[5]

I. Cadez, S. Gaffney, and P. Smyth. A general probabilistic framework for clustering individuals and objects. In KDD, pages 140--149, 2000.

Digital Library

[6]

I. Cadez, P. Smyth, and H. Mannila. Probabilistic modeling of transaction data with applications to profiling, visualization, and prediction. In KDD, pages 37--46, 2001.

Digital Library

[7]

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

[8]

T. Griffiths and Z. Ghahramani. Infinite latent feature models and the indian buffet process. In Y. Weiss, B. Schölkopf, and J. Platt, editors, NIPS 18, pages 475--482. MIT Press, Cambridge, MA, 2006.

[9]

K. Heller and Z. Ghahramani. A nonparametric bayesian approach to modeling overlapping clusters. In AISATS. The Society for Artificial Intelligence and Statistics, 2007.

[10]

K. A. Heller, S. Williamson, and Z. Ghahramani. Statistical models for partial membership. In ICML, pages 392--399, 2008.

Digital Library

[11]

S. Kullback and R. A. Leibler. On information and sufficiency. The Annals of Mathematical Statistics, 22(1):79--86, 1951.

[12]

J. S. Liu. The collapsed gibbs sampler in bayesian computations with applications to a gene regulation problem. Journal of the American Statistical Association, 89(427):958--966, 1994.

[13]

J. S. Liu, W. H. Wong, and A. Kong. Covariance structure of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes. Biometrika, 81(1):27--40, 1994.

[14]

G. J. McLachlan and K. E. Basford. Mixture Models: Inference and Applications to Clustering. Marcel Dekker, New York, 1988.

[15]

G. J. McLachlan and D. Peel. Finite Mixture Models. Wiley, New York, 2000.

[16]

J. V. Neumann. Various techniques used in connection with random digits. Applied Math Series, 1951.

[17]

M. F. Porter. Snowball: A language for stemming algorithms. http://www.snowball.tartarus.org/texts/introduction.html, 2001.

[18]

A. E. Raftery and S. Lewis. How many iterations in the gibbs sampler? In Bayesian Statistics, volume 4, pages 763--773. Oxford University Press, 1992.

[19]

Reuters news and video archive. http://www.reuters.com/resources/archive/us/index.html.

[20]

C. P. Robert and G. Casella. Monte Carlo Statistical Methods. Springer, 2005.

Digital Library

[21]

M. Somaiya, C. Jermaine, and S. Ranka. Learning correlations using the mixture-of-subsets model. ACM Trans. Knowl. Discov. Data, 1(4):1--42, 2008.

Digital Library

[22]

M. Somaiya, C. Jermaine, and S. Ranka. Various experiments using POWER models. http://www.cise.ufl.edu/~ranka/power/, 2010.

Cited By

Gilpin SEliassi-Rad TDavidson IGrossman RUthurusamy RDhillon IKoren Y(2013)Guided learning for role discovery (GLRD)Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2487575.2487620(113-121)Online publication date: 11-Aug-2013
https://dl.acm.org/doi/10.1145/2487575.2487620
Gunnemann SFaloutsos C(2013)Mixed Membership Subspace Clustering2013 IEEE 13th International Conference on Data Mining10.1109/ICDM.2013.109(221-230)Online publication date: Dec-2013
https://doi.org/10.1109/ICDM.2013.109
Almutairi ASomaiya MMoghaddam SRanka SHelmy APazzi RCano J(2012)A global local modeling of internet usage in large mobile societiesProceedings of the 7th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks10.1145/2387191.2387202(69-76)Online publication date: 21-Oct-2012
https://dl.acm.org/doi/10.1145/2387191.2387202
Show More Cited By

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cover image ACM Conferences

KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining

July 2010

1240 pages

ISBN:9781450300551

DOI:10.1145/1835804

General Chairs:
Bharat Rao
Siemens
,
Balaji Krishnapuram
Siemens
,
Program Chairs:
Andrew Tomkins
Google Inc.
,
Qiang Yang
Hong Kong University of Science and Technology

Copyright © 2010 ACM.

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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Published: 25 July 2010

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KDD '10: The 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

July 25 - 28, 2010

DC, Washington, USA

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Cited By

Gilpin SEliassi-Rad TDavidson IGrossman RUthurusamy RDhillon IKoren Y(2013)Guided learning for role discovery (GLRD)Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2487575.2487620(113-121)Online publication date: 11-Aug-2013
https://dl.acm.org/doi/10.1145/2487575.2487620
Gunnemann SFaloutsos C(2013)Mixed Membership Subspace Clustering2013 IEEE 13th International Conference on Data Mining10.1109/ICDM.2013.109(221-230)Online publication date: Dec-2013
https://doi.org/10.1109/ICDM.2013.109
Almutairi ASomaiya MMoghaddam SRanka SHelmy APazzi RCano J(2012)A global local modeling of internet usage in large mobile societiesProceedings of the 7th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks10.1145/2387191.2387202(69-76)Online publication date: 21-Oct-2012
https://dl.acm.org/doi/10.1145/2387191.2387202
Henderson KGallagher BEliassi-Rad TTong HBasu SAkoglu LKoutra DFaloutsos CLi LYang QAgarwal DPei J(2012)RolXProceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2339530.2339723(1231-1239)Online publication date: 12-Aug-2012
https://dl.acm.org/doi/10.1145/2339530.2339723
Günnemann SFärber ISeidl TYang QAgarwal DPei J(2012)Multi-view clustering using mixture models in subspace projectionsProceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2339530.2339553(132-140)Online publication date: 12-Aug-2012
https://dl.acm.org/doi/10.1145/2339530.2339553

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