IMPLEMENTATION OF DYNAMIC AND FAST MINING ALGORITHMS ON INCREMENTAL DATASETS TO DISCOVER QUALITATIVE RULES-Reference-Cited by-同舟云学术

IMPLEMENTATION OF DYNAMIC AND FAST MINING ALGORITHMS ON INCREMENTAL DATASETS TO DISCOVER QUALITATIVE RULES

Published:2021-09-30 Issue:3 Volume:17 Page:82-91
ISSN:2353-6977
Container-title:Applied Computer Science
language:
Short-container-title:acs

Author:

Naresh Pannangi,Suguna R.

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.

Publisher

Politechnika Lubelska

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Computer Science Applications,Economics, Econometrics and Finance (miscellaneous),Mechanical Engineering,Biomedical Engineering,Information Systems,Control and Systems Engineering

Reference16 articles.

1. 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

2. 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

3. 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

4. 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

5. 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

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