Data Mining and Knowledge Discovery

, Volume 12, Issue 2–3, pp 181–201 | Cite as

Discovering Classification from Data of Multiple Sources

Original Paper


In many large e-commerce organizations, multiple data sources are often used to describe the same customers, thus it is important to consolidate data of multiple sources for intelligent business decision making. In this paper, we propose a novel method that predicts the classification of data from multiple sources without class labels in each source. We test our method on artificial and real-world datasets, and show that it can classify the data accurately. From the machine learning perspective, our method removes the fundamental assumption of providing class labels in supervised learning, and bridges the gap between supervised and unsupervised learning.


new solutions for multiple data source mining learning from multiple sources of data learning classifications from unlabeled data of multiple sources 



We thank Doug Fisher and Joel Martin for their extensive and insightful comments and suggestions on the earlier versions of the paper. We also thank Chenghui Li for discussions and working with CMS. Qiang Yang thanks the support of Hong Kong RGC grant HKUST 6187/04E.


  1. Blum, A. and Mitchell, T. 1998. Combining labeled and unlabeled data with co-training. In Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pp. 92–100.Google Scholar
  2. Cheeseman, P. and Stutz, J. 1996. Bayesian classification (AutoClass): Theory and results. In Advances in Knowledge Discovery and Data Mining, U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (Eds.), AAAI Press/MIT Press.Google Scholar
  3. Church, K.W. and Hanks, P. 1989. Word association norms, mutual information, and lexicography. In Proceedings of the 27th. Annual Meeting of the Association for Computational Linguistics, Vancouver, B.C. Association for Computational Linguistics, pp. 76–83.Google Scholar
  4. de Sa, V. 1994a. Learning classification with unlabeled data. In Advances in Neural Information Processing Systems, J. Cowan, G. Tesauro, and J. Alspector (Eds.), vol. 6, pp. 112–119.Google Scholar
  5. de Sa, V. 1994b. Minimizing disagreement for self-supervised classification. In Proceedings of the 1993 Connectionist Models Summer School, M. Mozer, P. Smolensky, D. Touretzky, and A. Weigend (Eds.), pp. 300–307.Google Scholar
  6. de Sa, V. and Ballard, D. 1998. Category learning through multi-modality sensing. Neural Computation, 10(5).Google Scholar
  7. Fisher, D. 1987. Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2:139–172.Google Scholar
  8. Kohavi, R. and John, G. 1997. Wrappers for feature subset selection. Artificial Intelligence, 97(1–2):273–324.Google Scholar
  9. Lu, S. and Chen, K. 1987. A machine learning approach to the automatic synthesis of mechanistic knowledge for engineering decision-making. Artificial Intelligence for Engineering Design, Analysis, and Manufacturing, 1:109–118.Google Scholar
  10. Murphy, P.M. and Aha, D.W. 1992. UCI Repository of Machine Learning Databases [Machine-readable data repository]. Irvine, CA, University of California, Department of Information and Computer Science.Google Scholar
  11. Nigam, K. and Ghani, R. 2000. Analyzing the effectiveness and applicability of co-training. In Proceedings of the Ninth International Conference on Information and Knowledge Management, pp. 86–93.Google Scholar
  12. Quinlan, J. 1993. C4.5: Programs for Machine Learning. San Mateo, CA, Morgan Kaufmann.Google Scholar
  13. Raskutti, B., Ferra, H., and Kowalczyk, A. 2002. Combining clustering and co-training to enhance text classification using unlabelled data. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 620–625.Google Scholar
  14. Reich, Y. 1992. Ecobweb: Preliminary user's manual. Tech. rep., Department of Civil Engineering, Carnegie Mellon University.Google Scholar
  15. Reich, Y. and Fenves, S. 1991. The formation and use of abstract concepts in design. In Concept Formation: Knowledge and Experience in Unsupervised Learning, D. Fisher, M. Pazzani, and P. Langley (Eds.), Morgan Kaufmann, CA.Google Scholar
  16. Reich, Y. and Fenves, S. 1992. Inductive learning of synthesis knowledge. International Journal of Expert Systems: Research and Applications, 5(4):275–297.Google Scholar
  17. Sinkkonen, J., Nikkil, J., Lahti, L., and Kaski, S. 2004. Associative clustering. In Proceedings of 15th European Conference on Machine Learning (ECML 2004), pp. 396–406.Google Scholar
  18. Turney, P. (1993). Exploiting context when learning to classify. In Proceedings of ECML-93, pp. 402–407.Google Scholar
  19. Wu, X. and Zhang, S. 2003. Synthesizing high-frequency rules from different data source. IEEE Transactions on Knowledge and Data Engineering, 15(2):353–367.Google Scholar
  20. Yao, Y., Chen, L., Goh, A., and Wong, A. 2002. Clustering gene data via associative clustering neural network. In Proceedings of the 9th International Conference on Neural Information Processing (ICONIP 2002), pp. 2228–2232.Google Scholar
  21. Zhang, S., Wu, X., and Zhang, C. 2003. Multi-database mining. IEEE Computational Intelligence Bulletin, 2(1):5–13.Google Scholar

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of Western OntarioLondonCanada
  2. 2.Department of Computer ScienceHong Kong USTKowloonHong Kong

Personalised recommendations