Implementation of Data Mining With lustering of Big data for Shopping mall’s data using SOM and K-means Algorithm
 |
International Journal of Computer Trends and Technology (IJCTT) |  |
| © 2019 by IJCTT Journal | ||
| Volume-67 Issue-12 |
||
| Year of Publication : 2019 | ||
| Authors : Fatema Jamnagarwala, Dr.P.A.Tijare | ||
| DOI : 10.14445/22312803/IJCTT-V67I12P102 |
MLA Style:Fatema Jamnagarwala, Dr.P.A.Tijare "Implementation of Data Mining With lustering of Big data for Shopping mall’s data using SOM and K-means Algorithm," International Journal of Computer Trends and Technology 67.12 (2019):3-7.
APA Style Fatema Jamnagarwala, Dr.P.A.Tijare. Implementation of Data Mining With lustering of Big data for Shopping mall’s data using SOM and K-means Algorithm International Journal of Computer Trends and Technology, 67(12),3-7.
Abstract
Study of customer behaviour in online shopping usually deals with identification of customers and their buying behaviour patterns. The aim of such studies is to make certain who buys where, what, when and how. The results of these studies are useful in the solution of marketing problems. Various studies on customer purchasing behaviours have been presented and used in real problems. For analysis of customer behaviours data mining techniques are consider more effective. The target of this paper is to analyze behaviour of such people who are visiting the online shopping sites and spending their time there, surfing for different stuff. It would also be taken into account that how many people are there and how many of them are actually shopping. In this paper, different queries are applied to mine the database of a specified site which results in analysis of customer behaviour towards online shopping.
Reference
[1] Xindong Wu, Fellow, IEEE, Xingquan Zhu, Senior Member, IEEE “Data Mining with Big Data” 1041-4347/14.
[2] Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome (2009). “The Elements of Statistical Learning: Data Mining, Inference and Prediction”. Retrieved 2012-08-07.
[3] Fayyad, Usama; Piatetsky-Shapiro, Gregory; Smyth, Padhraic. “From Data Mining to Knowledge Discovery in Databases”. Retrieved 17 December 2008.
[4] Arun K Pujari, “Data Mining Techniques”, University Press, second edition,2009
[5] Aggarwal, C. C. Charu and C. X. Zhai, Eds., “Chapter 4: A Survey of Text Clustering Algorithms” in Mining Text Data. NewYork: Springer, 2012.
[6] Laurent Galluccioa , Olivier Michelb, Pierre Comonb, Mark Kligerc, Alfred O. Herod, “Clustering with a new distance measure based on a dual-rooted tree”, Information Sciences Volume 251, 1 December 2013, Pages 96-113, Elsevier.
[7] Yiheng Chen and Bing Qin “The Comparison of SOM and K-means of text clustering” School of Computer Science and Technology, Harbin Institute of Technology
[8] “Data Mining Curriculum” ACM SIGKDD. 2006-04-30. Retrieved 2011-10-28.
[9] Clifton, Christopher (2010). "Encyclopædia Britannica: Definition of Data Mining". Retrieved 2010-12-09.
[10] X. Wu, “Building Intelligent Learning Database Systems,” AI Magazine, vol. 21, no. 3, pp. 61-67, 2000.
[11] Efraim, T.; Jay, E. A.; Tin-Peng, L. & Ramesh, S. (2007). “Decision Support and Business Intelligent Systems, Pearson Education”.
[12] Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome (2009). "The Elements of Statistical Learning: Data Mining, Inference and Prediction". Retrieved 2012-08-07.
Keywords
data mining; clustering, association rule; frequent item set;