Abstract
Mining class association rules with itemset constraints is very popular in mining medical datasets. For example, when classifying which populations are at high risk for the HIV infection, epidemiologists often concentrate on rules which include demographic information such as sex, age, and marital status in the rule antecedents. However, two existing methods, post-processing and pre-processing, require much time and effort. In this paper, we propose a lattice-based approach for efficiently mining class association rules with itemset constraints. We first build a lattice structure to store all frequent itemsets. We then use paternity relations among nodes to discover rules satisfying the constraint without re-building the lattice. The experimental results show that our proposed method outperforms other methods in the mining time.
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References
Vo, B., Le, B.: Mining traditional association rules using frequent itemsets lattice. In: The 39th International Conference on Computers & Industrial Engineering (CIE 2009), pp. 1401–1406. IEEE (2009)
Nguyen, D., Vo, B., Le, B.: Efficient Strategies for Parallel Mining Class Association Rules. Expert Systems with Applications 41, 4716–4729 (2014)
Van, T.-T., Vo, B., Le, B.: IMSR_PreTree: an improved algorithm for mining sequential rules based on the prefix-tree. Vietnam Journal of Computer Science 1, 97–105 (2014)
Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: The 4th International Conference on Knowledge Discovery and Data Mining (KDD 1998), pp. 80–86 (1998)
Vo, B., Le, B.: A novel classification algorithm based on association rules mining. In: Richards, D., Kang, B.-H. (eds.) PKAW 2008. LNCS, vol. 5465, pp. 61–75. Springer, Heidelberg (2009)
Nguyen, L.T., Vo, B., Hong, T.-P., Thanh, H.C.: CAR-Miner: An efficient algorithm for mining class-association rules. Expert Systems with Applications 40, 2305–2311 (2013)
Nguyen, D., Vo, B.: Mining class-association rules with constraints. In: Van Huynh, N., Denoeux, T., Tran, D.H., Le, A.C., Pham, B.S. (eds.) KSE 2013, Part II. AISC, vol. 245, pp. 323–334. Springer, Heidelberg (2014)
Srikant, R., Vu, Q., Agrawal, R.: Mining association rules with item constraints. In: The 3rd International Conference on Knowledge Discovery and Data Mining (KDD 1997), pp. 67–73 (1997)
Ng, R.T., Lakshmanan, L.V.S., Han, J., Pang, A.: Exploratory mining and pruning optimizations of constrained associations rules. In: ACM SIGMOD International Conference on Management of Data, pp. 13–24. ACM (1998)
Lin, W.-Y., Huang, K.-W., Wu, C.-A.: MCFPTree: An FP-tree-based algorithm for multi-constraint patterns discovery. International Journal of Business Intelligence and Data Mining 5, 231–246 (2010)
Nguyen, D., Truong, T., Vo, B.: Mining Frequent Patterns containing HIV-Positive in HIV Voluntary Counseling and Testing data. ICIC Express Letters 8, 541–546 (2014)
Duong, H., Truong, T., Vo, B.: An efficient method for mining frequent itemsets with double constraints. Engineering Applications of Artificial Intelligence 27, 148–154 (2014)
Vo, B., Le, T., Hong, T.-P., Le, B.: An effective approach for maintenance of pre-large-based frequent-itemset lattice in incremental mining. Applied Intelligence (in press, 2014)
Nguyen, L.T., Vo, B., Hong, T.-P., Thanh, H.C.: Classification based on association rules: A lattice-based approach. Expert Systems with Applications 37, 11357–11366 (2012)
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Nguyen, D., Vo, B., Le, B. (2014). A Novel Method for Mining Class Association Rules with Itemset Constraints. In: Hwang, D., Jung, J.J., Nguyen, NT. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2014. Lecture Notes in Computer Science(), vol 8733. Springer, Cham. https://doi.org/10.1007/978-3-319-11289-3_50
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DOI: https://doi.org/10.1007/978-3-319-11289-3_50
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11288-6
Online ISBN: 978-3-319-11289-3
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