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Reseach Article

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

by J. S. Kanchana, S. Sujatha
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 39 - Number 2
Year of Publication: 2012
Authors: J. S. Kanchana, S. Sujatha
10.5120/4790-7013

J. S. Kanchana, S. Sujatha . A Comparative Analysis of Iterative Techniques Ensemble FSAC and Optimization Algorithms for E-Commerce Application. International Journal of Computer Applications. 39, 2 ( February 2012), 6-12. DOI=10.5120/4790-7013

@article{ 10.5120/4790-7013,
author = { J. S. Kanchana, S. Sujatha },
title = { A Comparative Analysis of Iterative Techniques Ensemble FSAC and Optimization Algorithms for E-Commerce Application },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 39 },
number = { 2 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 6-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume39/number2/4790-7013/ },
doi = { 10.5120/4790-7013 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:25:22.078278+05:30
%A J. S. Kanchana
%A S. Sujatha
%T A Comparative Analysis of Iterative Techniques Ensemble FSAC and Optimization Algorithms for E-Commerce Application
%J International Journal of Computer Applications
%@ 0975-8887
%V 39
%N 2
%P 6-12
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

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.

References
  1. Hoffman, K., Combinatorial Optimization: Current Successes and Directions for the Future. Journal of Computational and Applied Mathematics, 2000. 124: p. 341-360.
  2. Jiang, T. and A. Tuzhilin,” Segmenting Customers from Population to Individual Does 1-to-1 Keep Your Customers Forever?” IEEE TKDE, 2006. 18(10).
  3. Hoffman, K., Combinatorial Optimization: Current Successes and Directions for the Future. Journal of Computational and Applied Mathematics, 2000. 124: p. 341-360.
  4. Land, A.H. and A.G. Doig, “An automatic method for solving discrete programming problems” Econometrica, 1960. 28(97).
  5. Guignard, M. and S. Kim, Lagrangian decomposition: a model yielding stronger Lagrangian bounds. Mathematical Programming, 1987. 39: p. 215-228
  6. Gomory, R.E., Outline of an algorithm for integer solutions to linear programs. Bulletin American Mathematical Society, 1958. 64: p. 275-278
  7. Adomavicius, G. and A. Tuzhilin, Personalization technologies: “A process-oriented perspective. Communication of the CAM”, 2005.
  8. M. Ozdal and C. Aykanat, “Clustering Based on Data Patterns Using Hypergraph Models,” Data Mining and Knowledge Discovery, vol. 9, pp. 29-57, 2004.
  9. M. Pazzani and D. Billsus, “Learning and Revising User Profiles: The Identification of Interesting Web Sites,” Machine Learning, vol. 27, no. 3, pp. 313-331, 1997
  10. D. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, Sec. 6.3.2-6.3.3, MIT Press, 2001.
  11. B. Mobasher, H. Dai, T. Luo, and M. Nakagawa, “Using Sequential and Non-Sequential Patterns for Predictive Web Usage Mining Tasks,” Proc. IEEE Int’l Conf. Data Mining (ICDM), 2002.
  12. M. Spiliopoulou, B. Mobasher, B. Berendt, and M. Nakagawa, “A Framework for the Evaluation of Session Reconstruction Heuristics in Web Usage Analysis,” INFORMS J. Computing, no. 2, p. 15, 2003.
  13. C. Cortes, K. Fisher, D. Pregibon, A. Rogers, and F. Smith, “Hancock: A Language for Extracting Signatures from Data Streams,” Proc. Sixth ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (KDD), 2000.
  14. G. Adomavicius and A. Tuzhilin, “Expert-Driven Validation of Rule-Based User Models in Personalization Applications,” Data Mining and Knowledge Discovery, vol. 5, nos. 1/2, pp. 33-58, 2001.
  15. O. Nasraoui, M. Soliman, E. Saka, A. Badia, and R. Germain, “A Web Usage Mining Framework for Mining Evolving User Profiles in Dynamic Web Sites,” IEEE Trans. Knowledge and Data Eng., vol. 20, no. 2, pp. 202-215, Feb. 2008.
  16. E. Manavoglu, D. Pavlov, and C.L. Giles, “Probabilistic User Behavior Models,” Proc. Third IEEE Int’l Conf. Data Mining (ICDM), 2003.
  17. P. Mitra, C. Murthy, and S.K. Pal, “Unsupervised Feature Selection Using Feature Similarity,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 3, pp. 301-312, Mar. 2002.
  18. Massih R. Amini 2000. Interactive Learning for text summarization, Proceedings of the PKDD workshop onMachine Learning and Textual InformationAccess. Massih R. Amini and Patrick Gallinari 2003.
  19. Semi- Supervised Learning with Explicit Misclassification Modeling, Proceedings of the 18th International Joint Conference on Artificial Intelligence, 555–560.
  20. J. Vesanto, SOM-based data visualization methods, Intell. Data Anal. 3 (2) (1999) 111–126.
  21. Marc Caillet, Jean-Franc¸ois Pessiot, Massih-Reza Amini and Patrick Gallinari. 2004. Unsupervised Learning with Term Clustering for Thematic Segmentation of Texts Proceedings of the 7th Recherche d’Information Assiste par Ordinateur (RIAO’04), 648–656.
Index Terms

Computer Science
Information Sciences

Keywords

Mean square error Dunn’s Index Chi Square Test.