Unique Constrained Class labeled Association Rule Mining
Article No.: 119, Pages 1 - 5
Abstract
Class labeled Association Rules (CARs) represents the relationship between attribute-valued pairs and frequent item set mining. These rules are mostly used in diabetes healthcare management for reducing the uncertainty factor of co-morbidities. Moreover the user selects mostly subsets of class based association rules. In this paper we proposed an algorithm named as unique constraint class labeled association rule based tree (UCCAR-tree), which contains three steps. In the first step identifying frequent unique item set constrained. Later, UCCAR tree is constructed. Finally Class labeled Association Rules are obtained from the tree by satisfying minimum confidence user specified threshold value. The experimental results are performed based on German and Chess datasets. The execution time and the scalability of UCCAR for both the datasets are experimented along with describing the characteristics of dataset. Diabetes healthcare management application is demonstrated for the proposed algorithm.
References
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Centers for Disease Control and Prevention. "National diabetes fact sheet: National estimates and general infor- mation on diabetes and prediabetes in the United States," U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, 2011 {Online}. Available: http://www.cdc.gov/diabetes/pubs/factsheet11.htm
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Nguyen, L. T., Vo, B., Hong, T.-P., & Thanh, H. C. (2012). Classification based on association rules: A lattice-based approach. Expert Systems with Applications, 39, 11357--11366.
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P. W. Wilson et al., "Pediction of incident diabetes mellitus in middle-aged adults-the framingham offspring study," Arch. Intern. Med., vol. 167, no. 10, pp. 1068--1074, May 2007.
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Schlegel, B., Karnagel, T., Kiefer, T., & Lehner, W. (2013). Scalable frequent itemset mining on many-core processors. In The 9th International Workshop on Data Management on New Hardware. ACM. Article No. 3.
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Srikant, R., Vu, Q., Agrawal, R., 1997. Mining association rules with item constraints. In: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining (KDD 1997), pp. 67--73.
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- Unique Constrained Class labeled Association Rule Mining
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Published In
March 2016
843 pages
ISBN:9781450339629
DOI:10.1145/2905055
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Association for Computing Machinery
New York, NY, United States
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Published: 04 March 2016
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ICTCS '16
ICTCS '16: Second International Conference on Information and Communication Technology for Competitive Strategies
March 4 - 5, 2016
Udaipur, India
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Overall Acceptance Rate 97 of 270 submissions, 36%
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