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

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. Experimental results show effective formation of data with different attribute relations.
Further improvement of proposed approach is to support optimize attribute relations between different
item sets from large high dimensional datasets.