IMPLEMENTATION OF DYNAMIC AND FAST MINING ALGORITHMS ON INCREMENTAL DATASETS TO DISCOVER QUALITATIVE RULES
Pannangi Naresh
pannanginaresh@gmail.comVel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai (India)
R. Suguna
Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai (India)
Abstract
Association Rule Mining is an important field in knowledge mining that allows the rules of association needed for decision making. Frequent mining of objects presents a difficulty to huge datasets. As the dataset gets bigger and more time and burden to uncover the rules. In this paper, overhead and time-consuming overhead reduction techniques with an IPOC (Incremental Pre-ordered code) tree structure were examined. For the frequent usage of database mining items, those techniques require highly qualified data structures. FIN (Frequent itemset-Nodeset) employs a node-set, a unique and new data structure to extract frequently used Items and an IPOC tree to store frequent data progressively. Different methods have been modified to analyze and assess time and memory use in different data sets. The strategies suggested and executed shows increased performance when producing rules, using time and efficiency.
Keywords:
frequent itemset, nodeset, FIN and IPOCReferences
Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD international conference on Management of data – SIGMOD '93 (pp. 207–216). ACM Digital Library. https://doi.org/10.1145/170035.170072
DOI: https://doi.org/10.1145/170035.170072
Google Scholar
Deng, Z., Wang, Z., & Jiang, J. (2012). A new algorithm for fast mining frequent itemsets using N-lists. Science China Information Sciences, 55(9), 2008–2030. https://doi.org/10.1007/s11432-012-4638-z
DOI: https://doi.org/10.1007/s11432-012-4638-z
Google Scholar
Deng, Z., & Lv, S. (2015). PrePost+: An efficient N-lists-based algorithm for mining frequent itemsets via children–parent equivalence pruning. Expert Systems with Applications, 42(13), 5424–5432. https://doi.org/ 10.1016/j.eswa.2015.03.004
DOI: https://doi.org/10.1016/j.eswa.2015.03.004
Google Scholar
Hong, T.-P., Chen, H.-Y., Lin, Ch.-W., & Li, S.-T. (2008). Incrementally fast updated sequential pattern trees. 2008 International Conference on Machine Learning and Cybernetics (pp. 3991–3996). IEEE. https://doi.org/10.1109/icmlc.2008.4621100
DOI: https://doi.org/10.1109/ICMLC.2008.4621100
Google Scholar
Lv, D., Fu, B., Sun, X., Qiu, H., Liu, X., & Zhang, Y. (2017). Efficient fast updated frequent pattern tree algorithm and its parallel implementation. 2017 2nd International Conference on Image, Vision and Computing (ICIVC) (pp. 970-974). IEEE. https://doi.org/10.1109/icivc.2017.7984699
DOI: https://doi.org/10.1109/ICIVC.2017.7984699
Google Scholar
Naresh, P., & Suguna, R. (2019). Association rule mining algorithms on large and small datasets: A comparative study. 2019 International Conference on Intelligent Computing and Control Systems (ICCS) (pp. 587–592). IEEE. https://doi.org/10.1109/iccs45141.2019.9065836
DOI: https://doi.org/10.1109/ICCS45141.2019.9065836
Google Scholar
Pavitra Bai, S., & Ravi Kumar, G. K. (2016). Efficient incremental Itemset tree for approximate frequent Itemset mining on data stream. 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT) (pp. 239–242). IEEE. https://doi.org/10.1109/icatcct.2016.7912000
DOI: https://doi.org/10.1109/ICATCCT.2016.7912000
Google Scholar
Qu, J., Hang, B., Wu, Z., Wu, Z., Gu, Q., & Tang, B. (2020). Efficient mining of frequent Itemsets using only one dynamic prefix tree. IEEE Access, 8, 183722-183735. https://doi.org/10.1109/access.2020.3029302
DOI: https://doi.org/10.1109/ACCESS.2020.3029302
Google Scholar
Maw, S. S. (2020). An improvement of FP-growth mining algorithm using linked list. 2020 IEEE Conference on Computer Applications (ICCA) (pp. 1–4). IEEE. https://doi.org/10.1109/icca49400.2020.9022857
DOI: https://doi.org/10.1109/ICCA49400.2020.9022857
Google Scholar
Chen, R., Zhao, S., & Liu, M. (2020). A fast approach for up-scaling frequent Itemsets. IEEE Access, 8, 97141–97151. https://doi.org/10.1109/ACCESS.2020.2995719
DOI: https://doi.org/10.1109/ACCESS.2020.2995719
Google Scholar
Jain, T., & Sharma, D. V. (2016). Quantitative analysis of Apriori and eclat algorithm for association rule mining. International Journal Of Engineering And Computer Science, 4(10). https://doi.org/10.18535/ijecs/v4i10.18
DOI: https://doi.org/10.18535/ijecs/v4i10.18
Google Scholar
Dhanaseelan, F. R., & Sutha, M. J. (2016). An effective hashtable-based approach for incrementally mining closed frequent itemsets using sliding Windows. International Journal of Data Mining, Modelling and Management, 8(4), 382. https://doi.org/10.1504/ijdmmm.2016.10002313
DOI: https://doi.org/10.1504/IJDMMM.2016.10002313
Google Scholar
Abdelhamid, E., Canim, M., Sadoghi, M., Bhattacharjee, B., Chang, Y., & Kalnis, P. (2017). Incremental frequent Subgraph mining on large evolving graphs. IEEE Transactions on Knowledge and Data Engineering, 29(12), 2710–2723. https://doi.org/10.1109/tkde.2017.2743075
DOI: https://doi.org/10.1109/TKDE.2017.2743075
Google Scholar
Song, W., & Rong, K. (2018). Mining high utility sequential patterns using maximal remaining utility. In Y. Tan, Y. Shi & Q. Tang (Eds.), Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science (Vol. 10943, pp. 466–477). Springer. https://doi.org/10.1007/978-3-319-93803-5_44
DOI: https://doi.org/10.1007/978-3-319-93803-5_44
Google Scholar
Han, J., Pei, J., Yin, Y., & Mao, R. (2004). Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery, 8(1), 53–87. https://doi.org/10.1023/b:dami.0000005258.31418.83
DOI: https://doi.org/10.1023/B:DAMI.0000005258.31418.83
Google Scholar
UCI machine learning repository: Data sets. (n.d.). Retrieved April 8, 2021 from https://archive.ics.uci.edu/ml/datasets
Google Scholar
Authors
Pannangi Nareshpannanginaresh@gmail.com
Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai India
Authors
R. SugunaVel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai India
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