Classification of Association Item Sets From Large Data Sets Based on User Awareness Using Hybrid
Srihari Varma Mantena1, CVPR Prasad2
1Srihari Varma Mantena, Department of CSE, Acharya Nagarjuna University, Guntur (A.P), India.
2Dr. CVPR Prasad, Department of CSE, Acharya Nagarjuna University, Guntur (A.P), India.
Manuscript received on 18 August 2019 | Revised Manuscript received on 29 August 2019 | Manuscript Published on 06 September 2019 | PP: 1055-1061 | Volume-8 Issue- 6S, August 2019 | Retrieval Number: F12010886S19/19©BEIESP | DOI: 10.35940/ijeat.F1201.0886S19
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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: In business intelligence, large number of data to be generated because of increasing data in business applications. Analysis and prediction of data is very aggressive concept to evaluate the results present in data based on decision making analysis. To provide effective analysis of data traditionally some of the machine learning related methods like Clustering, Classification, Neural network based approaches and association rule based approaches were used to explore and analysis of business data. Because of increasing depth analysis of data in business intelligence related applications then above static machine learning approaches were not satisfied to form association between different attributes in real time data sets. So that in this paper, we propose Advanced & Hybrid Machine Learning Approach (AHMLA) for effective data analysis of different associated attributes of high dimensional data. Our proposed approach increase customer service, report generations based on user awareness in business intelligence applications. An experimental result of proposed approach gives better high performance with respect to different parameters with respect to existing approaches.
Keywords: Business Intelligence, Information Retrieval, Attribute Classification, Services of Customers, and Customer Report Generations.
Scope of the Article: Classification