Paper Title : Customer Classification Of Discrete Customer Assets Data And Re-Ranking Of Classified Data

ISSN : 2394-2231
Year of Publication : 2020

10.29126/23942231/IJCT-V7I2P3

Authors: Ms. N. Zahira Jahan, M.C.A., M. Phil ., Mr. T. Sasitharan

         



MLA Style: Ms. N. Zahira Jahan, M.C.A., M. Phil ., Mr. T. Sasitharan, "Customer Classification Of Discrete Customer Assets Data And Re-Ranking Of Classified Data" Volume 7 - Issue 2 March - April,2020 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

APA Style: Ms. N. Zahira Jahan, M.C.A., M. Phil ., Mr. T. Sasitharan, "Customer Classification Of Discrete Customer Assets Data And Re-Ranking Of Classified Data" Volume 7 - Issue 2 March - April,2020 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

Abstract
The project “CUSTOMER CLASSIFICATION OF DISCRETE CUSTOMER ASSETS DATA AND RE-RANKING OF CLASSIFIED DATA”. Selecting useful information under the background of big data can help enterprises to classify customers more accurately. Outlier data includes important customer information. In order to study customer classification problem based on customer asset outlier data, a customer classification model based on outlier data analysis concerning customer asset is constructed successfully. The model is based on Variables in 4 dimensions including transaction frequency, types of products or services traded, transaction amount and client age. And using clustering before classification to divide twenty-five types of outlier customer data into four categories and corresponding marketing strategies also are put forward according to different classification of outlier customer data of a company. In addition, it also presents a flexible and effective re-ranking method, called CR-Re-ranking, to improve the retrieval effectiveness. To offer high accuracy on the top-ranked results, multi modal fusion re-ranking approach is used. Experimental results show that the quality, especially on the top-ranked results, is improved significantly.

Reference
[1] Peng, Yuhua & Yang, Xiaolan & Xu, Wenli. (2018). Optimization Research of Decision Support System Based on Data Mining Algorithm. Wireless Personal Communications. 102. 10.1007/s11277-018-5315-3. Article in Wireless Personal Communications 102(4) · January 2018 withDOI: 10.1007/s11277-018-5315-3. 14 Reads [2] Li Ju, Xu Wenbin, and Zhou Bei, Member, IACSIT. “Construction of Customer Classification Model Based on Inconsistent Decision Table” International Journal of e-Education, e-Business, e-Management and eLearning, Vol. 1, No. 3, August 2011 [3] Er. Jyoti, Er.Amandeep Singh Walia. “Recommendation system with Automated Web Usage data mining using K-Nearest Neighbor(KNN) classification” International Journal of Advanced Research in Computer Science. Volume 8, No. 4, May 2017 (Special Issue). ISSN No. 0976-5697 [4] Y. Cao, J. Xu, T.-Y. Liu, H. Li, Y. Huang, and H.-W. Hon, “Adapting Ranking SVM to Document Retrieval,” Proc. 29th Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval, 2006. [5] A. Amir, J. Argillander, M. Campbell, A. Haubold, G. Iyengar, S. Ebadollahi, F. Kang, M.R. Naphade, A. Natsev, J.R. Smith, J. Te si c, and T. Volkmer, “IBM Research TRECVID-2005 Video Retrieval System,” TREC Video Retrieval Evaluation Online Proc., 2005

Keywords
Data mining, customer clustering and I-Miner