Classification Of Association Item Sets From Large Data Sets Based On User Awareness Using Hybrid

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.