Downloads: 109 | Views: 148
Research Paper | Computer Science & Engineering | India | Volume 3 Issue 12, December 2014
A Perceptual Evaluation of Optimization Algorithms and Iterative Method for E-Commerce
Nikhat Akhtar | Dr. Devendera Agarwal
Abstract: In the age of digital and network, every high efficiency and high profit activity has to harmonize with internet. The business behaviors and activities always are the precursor for getting high efficiency and high profit. Consequently, each business behavior and activities have to adjust for integrating with internet. Underlay on the internet, business extension and promotion behaviors and activities general are called the Electronic Commerce (E-commerce). The quality of web-based customer service is the capability of a firms website to provide individual heed and attention. Today scenario personalization has become a vital business problem in various e-commerce applications, ranging from various dynamic web content presentations. In our paper Iterative technique partitions the customer in terms of frankly combining transactional data of various consumers that forms dissimilar customer behavior for each group, and best customers are acquired, by applying approach such as, IE (Iterative Evolution), ID (Iterative Diminution) and II (Iterative Intermingle) algorithm. The excellence of clustering is improved via Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). In this paper these two algorithms are compared and it is found that Iterative technique chorus Particle Swarm Optimization (PSO) is better than the other Ant Colony Optimization (ACO) algorithms. Additionally the results show that the Particle Swarm Optimization (PSO) algorithm outperforms other Ant Colony Optimization (ACO) algorithms methods. Finally quality is superior along with this response time higher and cost wise performance is increased and both accuracy and efficiency.
Keywords: E-Commerce, Ant Colony Optimization ACO, Clustering, Preprocessing, Davies-Bouldins Index, Particle Swarm Optimization PSO
Edition: Volume 3 Issue 12, December 2014,
Pages: 2527 - 2534