A Preference Compositional Approach for Client Structured Web Customer Segmentation Using Machine Learning Techniques

Abstract

The web information system develops in an exponential growth in which the data classification and segmentation are tedious process to handle in an effective way.  The process of handling vast amount of web information with the target of segmented grouping entirely depends on the nature of the data along with the approach of segmentation.  The existing customer segmentation methods lacks in the areas of scale, modification and verification.  The main issues of redundancy, incorrect and irrelevant data plays its substantial role in degrading the performance of segmentation methodology.  This research article proposes a machine learning approach for handling client structured web customer segmentation with the preference compositional process based on their requirements of online web requests and responses.  In near future this research article leads the path for the incorporation of artificial intelligence based customer segmentation in web information system.