Extraction of Association Rule Mining using Apriori Algorithm with Wolf Search Optimisation in R Programming
Garima Jain1, Diksha Maurya2
1Garima Jain, Swami Vivekanand Subharti University, Meerut (Uttar Pradesh), India.
2Diksha Maurya, Swami Vivekanand Subharti University, Meerut (Uttar Pradesh), India.
Manuscript received on 05 August 2019 | Revised Manuscript received on 28 August 2019 | Manuscript Published on 05 September 2019 | PP: 504-507 | Volume-8 Issue-2S7 July 2019 | Retrieval Number: B10940782S719/2019©BEIESP | DOI: 10.35940/ijrte.B1094.0782S719
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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: Association rules mining (ARM) is a standout amongst the most essential Data Mining Systems. Find attribute patterns as a binding rule in a data set. The discovery of these suggestion rules would result in a mutual method. Firstly, regular elements are produced and therefore the association rules are extracted. In the literature, different algorithms inspired by nature have been proposed as BCO, ACO, PSO, etc. to find interesting association rules. This article presents the performance of the ARM hybrid approach with the optimization of wolf research based on two different fitness functions. The goal is to discover the best promising rules in the data set, avoiding optimal local solutions. The implementation is done in numerical data that require data discretization as a preliminary phase and therefore the application of ARM with optimization to generate the best rules.
Keywords: Association Rule Mining, Apriori Algorithm, Fitness Function, Wolf Search Optimization.
Scope of the Article: Algorithm Engineering