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Associative categorization of frequent patterns based on the probabilistic graphical model

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Abstract

Discovering the hierarchical structures of different classes of object behaviors can satisfy the requirements of various degrees of abstraction in association analysis, behavior modeling, data preprocessing, pattern recognition and decision making, etc. In this paper, we call this process as associative categorization, which is different from classical clustering, associative classification and associative clustering. Focusing on representing the associations of behaviors and the corresponding uncertainties, we propose the method for constructing a Markov network (MN) from the results of frequent pattern mining, called item-associative Markov network (IAMN), where nodes and edges represent the frequent patterns and their associations respectively. We further discuss the properties of a probabilistic graphical-model to guarantee the IAMN’s correctness theoretically. Then, we adopt the concept of chordal to reflect the closeness of nodes in the IAMN. Adopting the algorithm for constructing join trees from an MN, we give the algorithm for IAMN-based associative categorization by hierarchical bottom-up aggregations of nodes. Experimental results show-the effectiveness, efficiency and correctness of our methods.

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References

  1. Liu W, Yue K, Wu T, Wei M. An approach for multi-objective catego rization based on the game theory and Markov process. Applied Soft Computing, 2011, 11(6): 4087–4096

    Article  Google Scholar 

  2. Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 1993, 207–216

    Chapter  Google Scholar 

  3. Han J, Kamber M. Data Mining: Concepts and Techniques. 1st ed. Morgan Kaufmann, 2000

    Google Scholar 

  4. Han J, Cheng H, Xin D, Yan X. Frequent pattern mining: current status and future directions. Data Mining and Knowledge Discovery, 2007, 15(1): 55–85

    Article  MathSciNet  Google Scholar 

  5. Chaoji V, A I Hasan M, Salem S, Zaki M. An integrated, generic approach to pattern mining: data mining template library. Data Mining and Knowledge Discovery, 2008, 17(1): 457–495.

    Article  MathSciNet  Google Scholar 

  6. Sudhamathy G, Venkateswaran C. An efficient hierarchical frequent pattern analysis approach for web usage mining. International Journal of Computer Applications, 2012, 43(15): 1–7

    Article  Google Scholar 

  7. Ji L, Tan K, Tung A. Compressed hierarchical mining of frequent closed patterns from dense data sets. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(9): 1175–1187

    Article  Google Scholar 

  8. Cui P, Liu Z, Sun L, Yang S. Hierarchical visual event pattern mining and its applications. Data Mining and Knowledge Discovery, 2011, 22(1): 467–492.

    Article  MATH  MathSciNet  Google Scholar 

  9. Nguyen V, Yamamoto A. Mining of closed frequent subtrees from frequently updated databases. Intelligent Data Analysis, 2012, 16(6): 953–967

    Google Scholar 

  10. Jain A. Data clustering: 50 years beyond k-means. Pattern Recognition Letters, 2010, 31(8): 651–666

    Article  Google Scholar 

  11. Forestier G, Gancarski P, Wemmert C. Collaborative clustering with background knowledge. Data and Knowledge Engineering, 2010, 69(2): 211–228

    Article  Google Scholar 

  12. Thabtah F. A review of associative classification mining. The Knowledge Engineering Review, 2007, 22(1): 37–65

    Article  Google Scholar 

  13. Baralis E, Garza P. I-prune: item selection for associative classification. International Journal of Intelligent Systems, 2012, 27(1): 279–299

    Article  Google Scholar 

  14. Wang X, Yue K, Niu W, Shi Z. An approach for adaptive associative classification. Expert Systems with Applications, 2011, 38(9): 11873–11883

    Article  Google Scholar 

  15. Lucas J, Laurent A, Moreno M, Teisseire M. A fuzzy associative classification approach for recommender systems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2012, 20(4): 579–617

    Article  MathSciNet  Google Scholar 

  16. Sinkkonen J, Nikkilä J, Lahti L, Kaski S. Associative clustering. Lecture Notes in Computer Science, 2004, 3201: 396–406

    Article  Google Scholar 

  17. Kaski S, Nikkila J, Sinkkonen J, Lahti L, Knuuttila J, Roos C. Associative clustering for exploring dependencies between functional genomics data sets. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2005, 2(1): 203–216

    Article  Google Scholar 

  18. Pearl J. Probabilistic Reasoning in Intelligent Systems: Network of Plausible Inference. San Mateo: Morgan Kaufmann, 1988

    Google Scholar 

  19. Wong S, Butz C. Constructing the dependency structure of a multiagent probabilistic network. IEEE Transactions on Knowledge and Data Engineering, 2001, 13(1): 395–415.

    Article  Google Scholar 

  20. George D, Hawkins J. A hierarchical Bayesian model of invariant pattern recognition in the visual cortex. In: Proceedings of the 2005 IEEE International Joint Conference on Neural Networks. 2005, 3: 1812–1817

    Article  Google Scholar 

  21. Hu C, Wu X, Hu X, Yao H. Computing and pruning method for frequent pattern interestingness based on Bayesian networks. Journal of Software, 2011, 22(12): 2934–2950

    Article  Google Scholar 

  22. Bowes J, Neufeld E, Greer J, Cooke J. A comparison of association rule discovery and Bayesian network causal inference algorithms to discover relationships in discrete data. Lecture Notes in Computer Science, 2000, 1822: 326–336

    Article  Google Scholar 

  23. Jaroszewicz S, Scheffer T. Fast discovery of unexpected patterns in data, relative to a Bayesian network. In: Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining. 2005, 118–127

    Google Scholar 

  24. Malhas R, Aghbari Z. Interestingness filtering engine: mining Bayesian networks for interesting patterns. Expert Systems with Applications, 2009, 36(1): 5137–5145

    Article  Google Scholar 

  25. Fauré C, Delprat S, Boulicaut J, Mille A. Iterative Bayesian network implementation by using annotated association rules. Lecture Notes in Computer Science, 2006, 4248: 326–333

    Article  Google Scholar 

  26. Yu K, Wu X, Ding W, Wang H, Yao H. Causal associative classification. In: Proceedings of the 11th IEEE International Conference on Data Mining. 2011, 914–923

    Google Scholar 

  27. ScienceDirect. http://www.sciencedirect.icom/, 2012

  28. Cheng J, Bell D. Liu W. Learning Bayesian network from data: an efficient approach based on information theory. In: Proceedings of the 1997 Conference on Information and Knowledge Management. 1997, 325–331

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650091, China

    Weiyi Liu, Kun Yue, Hui Liu & Ping Zhang

  2. Department of Economics, State University of New York at Binghamton, Binghamton, NY, 13902-6001, USA

    Suiye Liu

  3. Faculty of Economics and Administration, University of Malaya, Kuala Lumper, 50603, Malaysia

    Qianyi Wang

Authors
  1. Weiyi Liu
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  2. Kun Yue
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  3. Hui Liu
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  4. Ping Zhang
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  5. Suiye Liu
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  6. Qianyi Wang
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Corresponding author

Correspondence to Kun Yue.

Additional information

Weiyi Liu graduated from Huazhong University of Science and Technology, China in 1976. He has been a professor and PhD supervisor of Yunnan University, China. His current research interests mainly include artificial intelligence, data and knowledge engineering. He is an associate member of the IEEE Computer Society

Kun Yue graduated from Fudan University, China in 2004, and received an MS in Computer Science. He graduated from Yunnan University, China in 2009, and received a PhD in Computer Science. He has been a professor at Yunnan University. His research interests include data and knowledge engineering, Web data management, and uncertainty in artificial intelligence.

Hui Liu graduated from Yunnan University, China in 2013, and received an MS in computer science. His research interests include social network analysis and artificial intelligence.

Ping Zhang graduated from Yunnan University, China in 2013, and received an MS in computer science. Now he is currently a PhD candidate in Wuhan University, China. His research interests include massive data management and social network analysis.

Suiye Liu graduated from State University of New York at Binghamton, USA, and received a BS in Financial Economics and an MA in economics. Since then he has been working in financial service industry for 3 years and is currently pursuing his MBA degree. His research interests include financial data analysis and intelligent decision.

Qianyi Wang is an MS candidate of Faculty of Economics and Administration, University of Malaya, Malaysia. Her research interests include financial data analysis, macroeconomic, and political economy.

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Liu, W., Yue, K., Liu, H. et al. Associative categorization of frequent patterns based on the probabilistic graphical model. Front. Comput. Sci. 8, 265–278 (2014). https://doi.org/10.1007/s11704-014-3173-z

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