POSITIVE, NEGATIVE RULE AND APPLICATION
DOI:
https://doi.org/10.37569/DalatUniversity.8.3.437(2018)Keywords:
Minimal negative rule, Minimal positive rule, Minimal rule, Negative rule, Positive rule.Abstract
Positive and negative reasoning have been found to be very useful in practice, as is clear from the record of many real-life applications, especially in medicine. In this paper, we introduce the concepts of extended negative rule, minimal rule and their properties. Then, an algorithm to generate all minimal positive and minimal negative rules is introduced. Experimental results obtained on data sets from the UCI repository of machine learning databases and the result of an experiment performed on a real-world dataset, the teaching and learning database at Nhatrang University were discussed.Downloads
References
Alataş, B., & Akin, E. (2006). An efficient genetic algorithm for automated mining of both positive and negative quantitative association rules. Soft Computing, 10(3), 230-237.
Ji, L., & Tan, K. L. (2004). Mining gen expression data for positive and negative co-regulated gen clusters. Bioinformatics, 20, 2711-2718.
Lashin, E. F., Kozae, A. M., Khadra, A. A. A., & Medhat, T. (2005). Rough set theory for topological spaces. International Journal of Approximate Reasoning, 40(1-2), 35-43.
Ngo, C. L. (2003). A tolerance rough set approach to clustering Web search results. (Master thesis) The Informatics and Mechanics Warsaw University, Poland.
Nguyen, D. T. (2013). Some extensions of positive and negative rules for discovering basic interesting rules. International Journal of Intelligent Information Systems, 2(4), 64-69.
Shipra, S., & Vivek, J. (2015). Generating positive-negative rules using fuzzy FP-growth & Naïve Bayes. International Journal of Computer Science Engineering and Information Technology Research, 5(2), 31-40.
Shusaku, T. (2004). Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model. Information Sciences, 162(2), 65-80.
Shusaku, T. (2005). Discovery of positive and negative rules from medical databases based on rough sets. In S. Tsumoto, Advanced techniques in knowledge discovery and data mining (233-252). London, UK: Springer-Verlag.
Sonam, J., & Rajeev, G. V. (2015). Generating positive & negative rules using efficient apriori algorithm. International Journal of Advances in Electronics and Computer Science, 2(4), 94-98.
The UCI. (2018). Welcome to the UCI machine learning repository. Retrieved from http://mlearn.ics.uci.edu/mlrepository.html.
Tinghuai, M., Jiazhao, L., Mengmeng, C., & Wei, T. (2009). Inducing positive and negative rules based on rough set. Information Technology Journal 8(7), 1039-1043.
Wu, X., Zhang, C., & Zhang, S. (2004). Efficient mining of both positive and negative association rules. ACM Transactions on Information System, 22(3), 381-405.
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Copyright (c) 2018 Nguyễn Đức Thuần, Phạm Quang Tùng, Hồ Thị Thu Sa

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