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

Pannangi Naresh

pannanginaresh@gmail.com
Vel 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 IPOC

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Published
2021-09-30

Cited by

Naresh, P., & Suguna, R. . (2021). IMPLEMENTATION OF DYNAMIC AND FAST MINING ALGORITHMS ON INCREMENTAL DATASETS TO DISCOVER QUALITATIVE RULES. Applied Computer Science, 17(3), 82–91. https://doi.org/10.35784/acs-2021-23

Authors

Pannangi Naresh 
pannanginaresh@gmail.com
Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai India

Authors

R. Suguna 

Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai India

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