Efficient Algorithm for Mining High Utility Item Sets
Khanaa. V1, Kumaravel. A2, Thirunavukkarasu. S3
1Khanaa V Dean, Department of Information and Technology, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
2Kumaravel A, Assistant Professor, Department of Information and Technology, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
3Thirunavukkarasu S, Assistant Professor, Department of Information and Technology, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
Manuscript received on 14 September 2019 | Revised Manuscript received on 23 September 2019 | Manuscript Published on 10 October 2019 | PP: 656-659 | Volume-8 Issue-6S2, August 2019 | Retrieval Number: F11920886S219/19©BEIESP | DOI: 10.35940/ijeat.F1192.0886S219
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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: Efficient introduction of obvious things in savage datasets could be a key test for data mining. Assorted perspective for making high utility models have been held for the instigating years, and this raises different issues, for instance, the age of a more perceivable than common level of contender things for top utility things, and clearly wealth mining capacity to the degree speed and zone. The unessential tree structure that has beginning late been organized, i.e., FP-Tree and UP-Tree, holds information on get-together advancement and itemsets, mining results, and dependably abstains from checking the affirmed data. During this report to get a controlled far up-tree is seen, basically twofold checks the data to get the up-and-comer.
Keywords: Contender Pruning, Visit Itemsets, Utility Mining, High Utility Itemsets.
Scope of the Article: Optical and High-Speed Access Networks