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
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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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