Customer Data Clustering using Density based algorithm

  • Authors

    • B Sekhar Babu
    • P Lakshmi Prasanna
    • P Vidyullatha
    2018-05-31
    https://doi.org/10.14419/ijet.v7i2.32.13520
  • Data mining, customer clustering, density varying, high-value low-risk customers.

  • This paper is about Clustering different segments of customers and their patterns of behaviour over different time intervals which are a very important application for business to maintain Business to Customer (B2C) Relationship. For clustering different segments of customers the input data will be taken from various business organizations like smart retail stores, and other stores. We take the input data from a particular amount of time like a year's data. All this data will be taken from the organization's databases. It has been observed that maintaining the old customers generate more profit when compared to attracting new ones. So, Customer retention is the important factor in our project. The main objective is to identify the elements with high profit and value to group them into different clusters. This will help to identify the high-value low-risk customers.  From the results obtained, we can be able to propose some strategies to the organization that helps in retaining the old customers and improve the profits.

     

     

  • References

    1. [1] Dr. Sankar Rajagopal, “Clustomer data clustering using data Mining techniqueâ€, International Journal of Database Management Systems. 2011 Nov 3(4).

      [2] Richa Sharma, Bhawna Malik, Anant Ram, “Local Density Differ Spatial Clustering in Data Miningâ€, International Journal of Advanced Research in Computer Science and Software Engineering.2013 Mar, 3(3).

      [3] Narges Delafrooz and Elham Farzanfar, “Determining the customer lifetime value based on the benefit clustering in the Insurance industryâ€, Indian journal of Science and Technology.2016 Jan 9(1).

      [4] Chatti Subbalakshmi, G.Rama Krishna and S.Krishna Mohan Rao, “Classification of dynamic clustering based on Soft Computing. Indian journal of Science and Technology. 2015 Dec ,8(36).

      [5] M. Parimala, Daphne Lopez, “K-Neighbourhood Structural Similarity Approach for Spatial Clusteringâ€, . Indian journal of Science and Technology. 2015 Sep, 8(23).

      [6] K. Selvakumar, L.Sai Ramesh, A. Kannan, “Enhanced K-Means Clustering Algorithm for Evolving User Groupsâ€, Indian journal of Science and Technology. 2015 Sep ,8(24).

      [7] D.Muruga Radha Devi, P.Thambidurai, “Similarity Measurement in Recent Biased Time Series Databases using Different Clustering Methodsâ€,Indian journal of Science and Technology.2014 Feb, 7(2).

      [8] Zeying Li, “Research on customer segmentation in retailing based on clustering modelâ€, IEEE International Conference on Computer Science and Service system(CSSS). 2011 June 27-29.

      [9] Shaily, G.L,Mehul, P. Bharot, and Darshak B.Mehta, “Web Usage Mining to discover visitor group with common behaviour using DBSCAN clustering algorithmâ€, International Journal of Engineering and Innovative Technology.2013 Jan.

      [10] Anant Ram, Sunita Jalal, Anand Jalal, and Manoj kumar, “A density based algorithm for discovering density varied clusters in spatial databasesâ€, International journal of Computer Applications.2010 Jun 3().

      [11] Gan Teck Wei, Shirly K, Wahidah Hussain, and Zurinahni Zainol, “A study of customer behavior through Web Miningâ€, Journal of Information Sciences and Computing Technologies. 2015 Feb ,2(1).

      [12] I.Krishna Murthy, Data Mining- Statistics Applications, “A Key to Managerial Decision Makingâ€, indiastat.com, April-May 2010.

      [13] Vidyullatha pellakuri, 2 D Rajeswara Rao, p lakshmi Prasanna “a conceptual framework for approaching predictive modeling using multivariate regression analysis vs artificial neural networkâ€, journal of theoretical and applied information technology, ISSN: 1992-8645,20th july 2015. vol.77. no.2

      [14] Kim, Yong Seog, & Street, W. Nick, “An intelligent system for customer targeting: A data mining approach. Decision Support Systemsâ€, 37, 215–228: 2004.

      [15] vidyullatha pellakuri, D. Rajeswara rao, “progressive decision making in the department of cardiology by optimized rough set modelâ€, int j pharm bio sci 2016 april; 7(2): (b) 658 – 665, ISSN 0975-6299

      [16] A.K. Jain, M.N.Murthy, and P.J.Flyn, “Data clustering- a review†ACM Computing Surveys (CSUR).

      [17] Han Jia -Wei and Micheline Kamber, “Data Mining concepts and Techniquesâ€, Higher education Press: 2001.

      [18] Anusha M,V Srikanth, “Enhancement of Wireless Mesh Network using Cognitive Radio’s,†European Journal of Journal of Applied Sciences, Vol.7, No.3,pp.108-113,2015.

      [19] P. Lakshmi Prasanna, D.Rajeswara Rao, B.Sekhar Babu,Vidyullatha Pellakuri, “Big Data for Mobile Applications In Retail Marketâ€, ARPN Journal of Engineering and Applied Sciences, ISSN 1819-6608, VOL.10, NO. 18, OCTOBER 2015.

      [20] Anusha M,V Srikanth, “An Efficient MAC Protocol for reducing Channel Interference and Access Delay in Cognitive Radio Wireless Mesh Networksâ€, International Journal on Communications Antenna and Propagation (IRECAP), ol.6, No.1, 2016.

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  • How to Cite

    Sekhar Babu, B., Lakshmi Prasanna, P., & Vidyullatha, P. (2018). Customer Data Clustering using Density based algorithm. International Journal of Engineering & Technology, 7(2.32), 35-38. https://doi.org/10.14419/ijet.v7i2.32.13520