Multi-Layer Efficient Data Classification Methods for Enterprise Business Applications

The efficient maintenance and classification of huge amounts of data is a big challenge for the websites which provide services for online businesses. Many of the websites provide multiple services for the customer. In the present work, we have compared various machine learning-based classification methods for the efficient distribution of data. To effectively categorize the data, the Enterprise Interface (El) layer is suggested between the application layer and the physical layer. Methods based on global and local clustering are proposed for the effective distribution of the data in the El layer. For the effective classification of the merchandise as per the various client classes, we have collected four parameters/features from the mall's customers. We have utilized the K-Means clustering approach to efficiently divide classes (Global Clustering). Additionally, we have examined seven categories for the proper group selection and prediction of recently arrived customers. The performance comparison makes use of Naive Bayesian, Logistic, Decision Tree, Random Forest, Support Vector Machine (SVM), Kernel-SVM, and K-Nearest Neighbor algorithms. The results show that Naive Bayesian and Random Forest Classification approaches outperform other classification techniques. The results also show that the proposed method is better than the existing cluster cum classification method.