A Comparative Analysis of Iterative Techniques Ensemble FSAC and Optimization Algorithms for E-Commerce Application

Adaptation to individual preferences of user’s personalization is a prominent challenge for the expansion of business application. One important factor that determines the quality of web-based customer service is the ability of a firm’s website to provide individual caring and attention. The main objective of this research is to verify the impact of customer’s information privacy concerns on firm’s collection and use of consumer information for web-based personalization, where firms compete with different levels of ability in customer information utilization for personalization. Customer segmentation is achieved using direct grouping-based approach. In our paper Iterative technique partitions the customer in terms of directly combining transactional data of several consumers that forms different customer behaviour for each group, and best customers are obtained by applying approach such as IG (Iterative Growth), IR(Iterative Reduction) and IM(Iterative Merge) algorithm. The quality of clustering is improved via Ant Colony Optimization (ACO), Feature Selection aggregated Clustering approach (FSAC) and Particle Swarm Optimization (PSO).In this paper these three algorithms are compared and it is found that Iterative technique ensemble Feature selection aggregated clustering approach is better than the other two algorithms. Moreover the clustering quality is superior, along with this; response time is higher than the other algorithms.