Implementation of Improved Association Rule Mining Algorithms for Fast Mining with Efficient Tree Structures on Large Datasets
P.Naresh1, R.Suguna2
1P.Naresh, Research Scholar, Dept of CSE, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology (Deemed to be University), Chennai.
2Dr.R.Suguna , Professor, Dept of CSE, Vel Tech Rangarajan.
3Dr. Sagunthala R&D Institute of Science and Technology (Deemed to be University), Chennai.
Manuscript received on November 21, 2019. | Revised Manuscript received on December 30, 2019. | Manuscript published on December 30, 2019. | PP: 5136-5141 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B3876129219/2019©BEIESP | DOI: 10.35940/ijeat.B3876.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: ARM is a significant area of knowledge mining which enables association rules which are essential for decision making. Frequent itemset mining has a challenge against large datasets. As going on the dataset size increases the burden and time to discover rules will increase. In this paper the ARM algorithms with tree structures like FP-tree, FIN with POC tree and PPC tree are discussed for reducing overheads and time consuming. These algorithms use highly competent data structures for mining frequent itemsets from the database. FIN uses nodeset a unique and novel data structure to extract frequent itemsets and POC tree to store frequent itemset information. These techniques are extremely helpful in the marketing fields. The proposed and implemented techniques reveal that they have improved about performance by means of time and efficiency.
Keywords: Association Rule Mining, FP-tree, POC tree, PPC tree