Main Article Content

Femina T Bahari
Dr Sudheep Elayidom M


In this paper we propose an efficient fuzzy computational framework for customer segmentation model in credit analysis. Normally segmentation methods cannot perform complex analysis so as to obtain the customer segments with high value. If the knowledge of experts in the data domain can be imparted to the generation of segments, this can bring in better results in the performance of classification models. In our approach customer attributes are selected after knowledge experts analysis and are segmented based on the limits set by them on the real numerical values. For each segment we generate the segmentation rules with definition of fuzzy basic linguistic term set. Each linguistic term set is assigned to a fuzzy membership function to generate the segmentation function. Combining the segmentation rules and generated functions the real valued numerical attributes are converted to fuzzified values in the interval [0, 1]. Both linguistic and numeric information are aggregated by a series of computations and a 2-tuple linguistic value is generated for each attribute in the database. The same term after a series of computations can be used in many decision making problems as it suffers no loss of information.


Download data is not yet available.

Article Details

Author Biography

Femina T Bahari, Cochin University of Science and Technology, kerala, India

Department of computer Science & Engineering. Research Scholar


Konstantinos Tsiptis and Antonios Chorianopoulo, “Data Mining Techniques in CRM: Inside Customer Segmentation,†Wiley and Sons Ltd, 2009.

Hugh Wilson, Elizabeth Daniel and Malcolm McDonald, Factors for Success in Customer Relationship Management (CRM) Systems, Journal of Marketing Management, 2002, 18, 193-219.

Jan-Benedict E.M. Steenkamp, and FrenkelTerHofstede. "International market segmentation: issues and perspectives." International Journal of Research in Marketing 19, no. 3 (2002).

Brito, Pedro Quelhas, Carlos Soares, Sérgio Almeida, Ana Monte, and Michel Byvoet. "Customer segmentation in a large database of an online customized fashion business." Robotics and Computer-Integrated Manufacturing , Elsevier ,2015.

Cheng-Lung Huang , Mu-Chen Chen, Chieh-Jen Wang, Credit scoring with a data mining approach based on support vector machines. Expert Systems with Applications 33 (2007) 847–856, Elsevier,2007.

Pierpaolo D’Urso, Marta Disegna , Riccardo Massari, Linda Osti, Fuzzy segmentation in postmodern consumers Bozen Economics & Management Paper Series NO 20 / 2014.

Tuma, M. N., Decker, R., & Scholz, S. W. (2011). A survey of the challenges and pitfalls of cluster analysis application in market segmentation. International Journal of Market Research, 53 (3), 391–414,2011.

Wang, Y., Ma, X., Lao, Y., & Wang, Y. (2014). A fuzzy-based customer clustering approach with hierarchical structure for logistics network optimization. Expert Systems with Applications, 41 (2), 521–534.regarding real-life ( Y. Wang, Ma, Lao, & Wang, 2014)

L.A. Zadeh, The concept of a linguistic variable and its applications to approximate reasoning, Part I, Information Sciences 8( 1975) 199–249, Part II, 8, 301–357; Part III, 9, 43–80.

L.A. Zadeh, Fuzzy logic¼computing with words, IEEE Transactions on Fuzzy Systems 4 (2) (1996) 103–111

F. Herrera and L. Martínez, “An approach for combining linguistic and numerical information based on 2-tuple fuzzy linguistic representation model in decision-making,†Int. J. Uncertainty, Fuzziness, Knowledge-Based Syst., vol. 8, no. 5, pp. 539–562, 2000.

F. Herrera, E. Herrera-Viedma, and L.Martínez, “A fusion approach for managing multi-granularity linguistic terms sets in decision making,†Fuzzy Sets Syst., vol. 114, no. 1, pp. 43–58, 2000.