Feature Enriched Nonparametric Bayesian Co-clustering

  • Pu Wang
  • Carlotta Domeniconi
  • Huzefa Rangwala
  • Kathryn B. Laskey
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7301)


Co-clustering has emerged as an important technique for mining relational data, especially when data are sparse and high-dimensional. Co-clustering simultaneously groups the different kinds of objects involved in a relation. Most co-clustering techniques typically only leverage the entries of the given contingency matrix to perform the two-way clustering. As a consequence, they cannot predict the interaction values for new objects. In many applications, though, additional features associated to the objects of interest are available. The Infinite Hidden Relational Model (IHRM) has been proposed to make use of these features. As such, IHRM has the capability to forecast relationships among previously unseen data. The work on IHRM lacks an evaluation of the improvement that can be achieved when leveraging features to make predictions for unseen objects. In this work, we fill this gap and re-interpret IHRM from a co-clustering point of view. We focus on the empirical evaluation of forecasting relationships between previously unseen objects by leveraging object features. The empirical evaluation demonstrates the effectiveness of the feature-enriched approach and identifies the conditions under which the use of features is most useful, i.e., with sparse data.


Bayesian Nonparametrics Dirichlet Processes Co-clustering Protein-molecule interaction data 


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  1. 1.
    Agarwal, D., Chen, B.-C.: Regression-based latent factor models. In: Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining, pp. 19–28 (2009)Google Scholar
  2. 2.
    Antoniak, C.E.: Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems. The Annals of Statistics 2(6), 1152–1174 (1974)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Balabanovic, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40(3), 66–72 (1997)CrossRefGoogle Scholar
  4. 4.
    Blackwell, D., Macqueen, J.B.: Ferguson distributions via Pólya urn schemes. The Annals of Statistics 1, 353–355 (1973)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. Journal of Machine Learning Research 3(4-5), 993–1022 (2003)zbMATHGoogle Scholar
  6. 6.
    Chen, Y.-H., George, E.I.: A bayesian model for collaborative filtering. In: 7th International Workshop on Artificial Intelligence and Statistics (1999)Google Scholar
  7. 7.
    Dhillon, I.S., Mallela, S., Modha, D.S.: Information-theoretic co-clustering. In: Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining, pp. 89–98 (2003)Google Scholar
  8. 8.
    Dunson, D.B., Xue, Y., Carin, L.: The matrix stick-breaking process: Flexible Bayes meta-analysis. Journal of the American Statistical Association 103(481), 317–327 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Ferguson, T.S.: A Bayesian analysis of some nonparametric problems. The Annals of Statistics 1(2), 209–230 (1973)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    George, T., Merugu, S.: A scalable collaborative filtering framework based on co-clustering. In: Proceedings of the IEEE International Conference on Data Mining, pp. 625–628 (2005)Google Scholar
  11. 11.
    Hartigan, J.A.: Direct clustering of a data matrix. Journal of the American Statistical Association 67(337), 123–129 (1972)Google Scholar
  12. 12.
    Hofmann, T.: Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. 22, 89–115 (2004)CrossRefGoogle Scholar
  13. 13.
    Jacob, L., Hoffmann, B., Stoven, V., Vert, J.-P.: Virtual screening of GPCRs: an in silico chemogenomics approach. BMC Bioinformatics 9(1), 363 (2008)CrossRefGoogle Scholar
  14. 14.
    Jin, R., Si, L., Zhai, C.: A study of mixture models for collaborative filtering. Journal of Information Retrieval 9, 357–382 (2006)CrossRefGoogle Scholar
  15. 15.
    Khoshneshin, M., Street, W.N.: Incremental collaborative filtering via evolutionary co-clustering. In: Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys 2010, pp. 325–328. ACM, New York (2010)CrossRefGoogle Scholar
  16. 16.
    Lemire, D., Maclachlan, A.: Slope one predictors for online rating-based collaborative filtering. In: Proceedings of the SIAM Data Mining, SDM (2005)Google Scholar
  17. 17.
    Lim, Y.J., Teh, Y.W.: Variational Bayesian Approach to Movie Rating Prediction. In: Proceedings of KDD Cup and Workshop (2007)Google Scholar
  18. 18.
    Marlin, B.: Modeling user rating profiles for collaborative filtering. In: Advances in Neural Information Processing Systems (NIPS), vol. 17 (2003)Google Scholar
  19. 19.
    Meeds, E., Roweis, S.: Nonparametric Bayesian Biclustering. Technical Report UTML TR 2007-001, Department of Computer Science, University of Toronto (2007)Google Scholar
  20. 20.
    Neal, R.M.: Markov Chain Sampling Methods for Dirichlet Process Mixture Models. Journal of Computational and Graphical Statistics 9(2), 249–265 (2000)MathSciNetGoogle Scholar
  21. 21.
    Ning, X., Rangwala, H., Karypis, G.: Multi-assay-based structure activity relationship models: Improving structure activity relationship models by incorporating activity information from related targets. Journal of Chemical Information and Modeling 49(11), 2444–2456 (2009); PMID: 19842624CrossRefGoogle Scholar
  22. 22.
    Okuno, Y., Yang, J., Taneishi, K., Yabuuchi, H., Tsujimoto, G.: GLIDA: GPCR-ligand database for chemical genomic drug discovery. Nucleic Acids Research 34(suppl. 1), D673–D677 (2006)CrossRefGoogle Scholar
  23. 23.
    Papaspiliopoulos, O., Roberts, G.O.: Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models. Biometrika 95(1), 169–186 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Pitman, J., Yor, M.: The two-parameter Poisson-Dirichlet distribution derived from a stable subordinator. Annals of Probability 25(2), 855–900 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Porteous, I., Asuncion, A., Welling, M.: Bayesian matrix factorization with side information and dirichlet process mixtures. In: AAAI (2010)Google Scholar
  26. 26.
    Salakhyuditnov, R., Mnih, A.: Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo. In: International Conference on Machine Learning (2008)Google Scholar
  27. 27.
    Schafer, J.B., Konstan, J., Riedi, J.: Recommender systems in e-commerce. In: Proceedings of the ACM Conference on Electronic Commerce, pp. 158–166 (1999)Google Scholar
  28. 28.
    Sethuraman, J.: A constructive definition of Dirichlet priors. Statistica Sinica 4, 639–650 (1994)MathSciNetzbMATHGoogle Scholar
  29. 29.
    Shafiei, M., Milios, E.: Latent Dirichlet co-clustering. In: IEEE International Conference on Data Mining, pp. 542–551 (2006)Google Scholar
  30. 30.
    Shan, H., Banerjee, A.: Bayesian co-clustering. In: IEEE International Conference on Data Mining (2008)Google Scholar
  31. 31.
    Shan, H., Banerjee, A.: Generalized probabilistic matrix factorizations for collaborative filtering. In: Proceedings of the IEEE International Conference on Data Mining, pp. 1025–1030 (2010)Google Scholar
  32. 32.
    Sutskever, I., Salakhutdinov, R., Tenenbaum, J.: Modelling relational data using Bayesian clustered tensor factorization. In: Advances in Neural Information Processing Systems, vol. 22, pp. 1821–1828 (2009)Google Scholar
  33. 33.
    Symeonidis, P., Nanopoulos, A., Papadopoulos, A., Manolopoulos, Y.: Nearest-Biclusters Collaborative Filtering with Constant Values. In: Nasraoui, O., Spiliopoulou, M., Srivastava, J., Mobasher, B., Masand, B. (eds.) WebKDD 2006. LNCS (LNAI), vol. 4811, pp. 36–55. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  34. 34.
    Wale, N., Karypis, G.: AFGEN. Technical report, Department of Computer Science & Enigneering, University of Minnesota (2007),
  35. 35.
    Wang, P., Domeniconi, C., Laskey, K.: Latent Dirichlet Bayesian co-clustering. In: Proceedings of the European Conference on Machine Learning, pp. 522–537 (2009)Google Scholar
  36. 36.
    Xu, Z., Tresp, V., Yu, K., Kriegel, H.: Infinite hidden relational models. In: Proceedings of the International Conference on Uncertainity in Artificial Intelligence (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pu Wang
    • 1
  • Carlotta Domeniconi
    • 1
  • Huzefa Rangwala
    • 1
  • Kathryn B. Laskey
    • 1
  1. 1.George Mason UniversityFairfaxUSA

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