Mining predicate rules without minimum support threshold
DOI:
https://doi.org/10.48129/kjs.v48i4.9782Keywords:
association rule mining, coherent rules, inference rules, interestingness measure, minimum support threshold, predicate logic.Abstract
Association rule mining (ARM) is used for discovering frequent itemsets for interesting relationships of associative and correlative behaviors within the data. This gives new insights of great value, both commercial and academic. The traditional ARM techniques discover interesting association rules based on a predefined minimum support threshold. However, there is no known standard of an exact definition of minimum support and providing an inappropriate minimum support value may result in missing important rules. In addition, most of the rules discovered by these traditional ARM techniques refer to already known knowledge. To address these limitations of the minimum support threshold in ARM techniques, this study proposes an algorithm to mine interesting association rules supportlessly using predicate logic and a property of a proposed interestingness measure ( measure). The algorithm scans the database and uses measure’s property to search for interesting combinations. The selected combinations are mapped to pseudo-implications and inference rules of logic are used on the pseudo-implications to produce and validate the predicate rules. Experimental results of the proposed technique show better performance against state-of-the-art classification techniques, and reliable predicate rules are discovered based on the reliability differences of the presence and absence of the rule’s consequence.
References
Agrawal, R., Imieliński, T. & Swami, A. (1993) Mining association rules between sets of items in large databases. Acm sigmod record, 1993. ACM, 207-216.
Alatas, B. (2012) A novel chemistry based metaheuristic optimization method for mining of classification rules. Expert Systems with Applications, 39(12), 11080-11088.
Bache, K. & Lichman. (2013). UCI: Machine Learning Repository [Online]. Available: http://archive.ics.uci.edu/ml [Accessed].
Bagui, S. & Dhar, P. C. (2018) Mining positive and negative association rules in Hadoop's MapReduce environment. Proceedings of the ACMSE 2018 Conference, 2018. 1-1.
Balakrishna, E., Rama, B. & Nagaraju, A. (2019) Efficient Mining of Negative Association Rules Using Frequent Item Set Mining. First International Conference on Artificial Intelligence and Cognitive Computing, 2019. Springer, 709-716.
Baralis, E., Cerquitelli, T. & Chiusano, S. (2008) IMine: Index support for item set mining. IEEE Transactions on Knowledge and data engineering, 21(4), 493-506.
Bemarisika, P. & Totohasina, A. (2018) Erapn, an algorithm for extraction positive and negative association rules in Big Data. International Conference on Big Data Analytics and Knowledge Discovery, 2018. Springer, 329-344.
Bhatia, J. & Gupta, A. (2014) Mining of quantitative association rules in agricultural data warehouse: A road map. International Journal of Information Science and Intelligent System, 3(1), 187-198.
Bisht, S. & Samantaray, S. (2015) Extracting spatial association rules in remotely sensed data of yellow rust disease in wheat crop at Udham Singh Nagar. 2015 1st International Conference on Next Generation Computing Technologies (NGCT), 2015. IEEE, 847-851.
Borah, A. & Nath, B. (2018) Identifying risk factors for adverse diseases using dynamic rare association rule mining. Expert Systems with Applications, 113, 233-263.
Borgelt, C. (2012) Frequent item set mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(6), 437-456.
Buczak, A. L. & Gifford, C. M. (2010) Fuzzy association rule mining for community crime pattern discovery. ACM SIGKDD Workshop on Intelligence and Security Informatics, 2010. ACM, 2.
Ceruto, T., Lapeira, O., Tonch, A., Plant, C., Espin, R. & Rosete, A. (2014) Mining medical data to obtain fuzzy predicates. International Conference on Information Technology in Bio-and Medical Informatics, 2014. Springer, 103-117.
Chen, C.-H., Li, A.-F. & Lee, Y.-C. (2014) Actionable high-coherent-utility fuzzy itemset mining. Soft Computing, 18(12), 2413-2424.
De Bie, T. (2011) An information theoretic framework for data mining. Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 2011. 564-572.
Dechang, P. & Xiaolin, Q. (2008) A new fuzzy clustering algorithm on association rules for knowledge management. Information Technology Journal, 7(1), 119-124.
Fournier-Viger, P., Cheng, C., Lin, J. C.-W., Yun, U. & Kiran, R. U. (2019) TKG: Efficient Mining of Top-K Frequent Subgraphs. International Conference on Big Data Analytics, 2019. Springer, 209-226.
Fournier‐Viger, P., Lin, J. C. W., Vo, B., Chi, T. T., Zhang, J. & Le, H. B. (2017) A survey of itemset mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery.
Geetha, M. (2015) Implementation of Association Rule Mining for different soil types in Agriculture. International Journal of Advanced Research in Computer and Communication Engineering, 4(4), 520-522.
Harahap, M., Husein, A., Aisyah, S., Lubis, F. & Wijaya, B. (2018) Mining association rule based on the diseases population for recommendation of medicine need. Journal of Physics: Conference Series, 2018. IOP Publishing, 012017.
Hasan, M. M. & Mishu, S. Z. (2018) An Adaptive Method for Mining Frequent Itemsets Based on Apriori And FP Growth Algorithm. 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), 2018. IEEE, 1-4.
Hassani, H., Huang, X., Silva, E. S. & Ghodsi, M. (2016) A review of data mining applications in crime. Statistical Analysis and Data Mining: The ASA Data Science Journal, 9(3), 139-154.
Ikram, A. & Qamar, U. (2015) Developing an expert system based on association rules and predicate logic for earthquake prediction. Knowledge-Based Systems, 75, 87-103.
Jabbour, S., El Mazouri, F. E. & Sais, L. (2018) Mining Negatives Association Rules Using Constraints. Procedia Computer Science, 127, 481-488.
Koh, Y. S. (2008) Mining non-coincidental rules without a user defined support threshold. Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2008. Springer, 910-915.
Kong, H., An, D. & Ri, J. (2018) Itemsets of interest for negative association rules. arXiv preprint arXiv:1806.07084.
Li, J., Zhang, X., Dong, G., Ramamohanarao, K. & Sun, Q. (1999) Efficient mining of high confidence association rules without support thresholds. European Conference on Principles of Data Mining and Knowledge Discovery, 1999. Springer, 406-411.
Parfait, B., Harrimann, R. & André, T. (2018) An efficient approach for extraction positive and negative association rules from Big Data. International Cross-Domain Conference for Machine Learning and Knowledge Extraction, 2018. Springer, 79-97.
Qin, Z., Ren, K., Yu, T. & Weng, J. (2016) DPcode: privacy-preserving frequent visual patterns publication on cloud. IEEE Transactions on Multimedia, 18(5), 929-939.
Salah, S., Akbarinia, R. & Masseglia, F. (2017) A highly scalable parallel algorithm for maximally informative k-itemset mining. Knowledge and Information Systems, 50(1), 1-26.
Salam, A. & Khayal, M. S. H. (2012) Mining top− k frequent patterns without minimum support threshold. Knowledge and information systems, 30(1), 57-86.
Sim, A. T. H., Indrawan, M., Zutshi, S. & Srinivasan, B. (2010) Logic-based pattern discovery. IEEE Transactions on Knowledge and Data Engineering, 22(6), 798-811.
Zhao, L., Hao, F., Xu, T. & Dong, X. (2017) Positive and Negative Association Rules Mining for Mental Health Analysis of College Students. Eurasia Journal of Mathematics, Science and Technology Education, 13(8), 5577-5587.
Zuo, J., Tang, C. & Zhang, T. (2002) Mining predicate association rule by gene expression programming. International Conference on Web-Age Information Management, 2002. Springer, 92-103.
