A Descriptive Classification of Association Item Sets from Large Data Sets Based on User Awareness Using Hybrid Approach

There is a large amount of data to be generated in business intelligence due to rising data in business applications. Data interpretation and prediction is a very vigorous idea for determining the outcomes of data based on decision-making analysis. Some of the machine learning-related techniques such as clustering, grouping, neural network-based approaches and association rule-based approaches have historically been used to explore and analyse business data in order to provide efficient data processing. Because of increasing data analysis in business intelligence-related applications, static machine learning approaches were not satisfied with establishing an association in real-time data sets between different attributes. So we suggest Advanced & Hybrid Computer in this article, Learning Methodology (AHMLA) for efficient data analysis of various high-dimensional data related attributes. Our proposed plan improves customer experience, reporting generations of business intelligence applications based on user understanding. An experimental outcome of the proposed method offers better output with regard to various parameters with regard to current approaches. Experimental findings illustrate successful data creation with different relationships between attributes. A further enhancement of the proposed approach is to facilitate the optimization of attribute relationships from large high-dimensional datasets between different item sets.

Author (s) Details

Srihari Varma Mantena
Department of CSE, Acharya Nagarjuna University, Guntur, AP, India.

C. V. P. R. Prasad
Department of CSE, Acharya Nagarjuna University, Guntur, AP, India.

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