Application of a 3NN+1 based CBR system to segmentation of the notebook computers market

Case-based reasoning system (CBR) has been widely applied to the issue of market segmentation. Most of previous studies focused on dividing customers into two groups. Consequently, traditional voting method used for two groups in CBR would become inappropriate when one would like to divide customers into three groups through some segmentation variable. In this paper, a new voting method called 3NN+1 is proposed to bridge the gap. To make the inference of the 3NN+1 based CBR system more efficient, the features and instances (or cases) for reasoning is selected simultaneously by means of genetic algorithms. This new system is applied to a real case of notebook market to demonstrate its usefulness for market segmentation. From the results of the real case, it shows that the system would be valuable to enterprises, when dividing customers into three groups in compliance with their purchasing behaviors for developing marketing strategies.

[1]  Abraham Pizam,et al.  Big Spenders and Little Spenders in U.S. Tourism , 1979 .

[2]  Kyoung-jae Kim Artificial neural networks with evolutionary instance selection for financial forecasting , 2006, Expert Syst. Appl..

[3]  Se-Hak Chun,et al.  A new hybrid data mining technique using a regression case based reasoning: Application to financial forecasting , 2006, Expert Syst. Appl..

[4]  Lakhmi C. Jain,et al.  Nearest neighbor classifier: Simultaneous editing and feature selection , 1999, Pattern Recognit. Lett..

[5]  Filiberto Pla,et al.  Prototype selection for the nearest neighbour rule through proximity graphs , 1997, Pattern Recognit. Lett..

[6]  Miroslav Kubat,et al.  Selecting representative examples and attributes by a genetic algorithm , 2003, Intell. Data Anal..

[7]  C. Mok,et al.  Expenditure-based segmentation: Taiwanese tourists to Guam , 2000 .

[8]  Dirk Van den Poel,et al.  FACULTEIT ECONOMIE , 2007 .

[9]  Ingoo Han,et al.  A case-based reasoning system with the two-dimensional reduction technique for customer classification , 2007, Expert Syst. Appl..

[10]  Shu-Hsuan Chang,et al.  Applying case-based reasoning for product configuration in mass customization environments , 2005, Expert Syst. Appl..

[11]  Jack Sklansky,et al.  A note on genetic algorithms for large-scale feature selection , 1989, Pattern Recognition Letters.

[12]  田口 玄一,et al.  System of experimental design : engineering methods to optimize quality and minimize costs , 1987 .

[13]  Daniel M. Spotts,et al.  Segmenting Visitors To A Destination Region Based On The Volume Of Their Expenditures , 1991 .

[14]  SooCheong Jang,et al.  Heavy Spenders, Medium Spenders, and Light Spenders of Japanese Outbound Pleasure Travelers , 2001 .

[15]  T. Ravindra Babu,et al.  Comparison of genetic algorithm based prototype selection schemes , 2001, Pattern Recognit..

[16]  Mu-Chen Chen,et al.  Prediction model building and feature selection with support vector machines in breast cancer diagnosis , 2008, Expert Syst. Appl..

[17]  Susan Craw,et al.  Self-optimising CBR retrieval , 2000, Proceedings 12th IEEE Internationals Conference on Tools with Artificial Intelligence. ICTAI 2000.

[18]  Janet L. Kolodner,et al.  Case-Based Reasoning , 1988, IJCAI 1989.

[19]  David B. Skalak,et al.  Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms , 1994, ICML.

[20]  Dennis L. Wilson,et al.  Asymptotic Properties of Nearest Neighbor Rules Using Edited Data , 1972, IEEE Trans. Syst. Man Cybern..

[21]  W. Eric L. Grimson,et al.  Prototype optimization for nearest-neighbor classification , 2002, Pattern Recognit..

[22]  Chaochang Chiu,et al.  A case-based customer classification approach for direct marketing , 2002, Expert Syst. Appl..

[23]  Kyoung-jae Kim,et al.  Global optimization of case-based reasoning for breast cytology diagnosis , 2009, Expert Syst. Appl..

[24]  S. Wesley Changchien,et al.  Design and implementation of a case-based reasoning system for marketing plans , 2005, Expert systems with applications.

[25]  Janet L. Kolodner,et al.  Improving Human Decision Making through Case-Based Decision Aiding , 1991, AI Mag..

[26]  Claire Cardie,et al.  Improving Minority Class Prediction Using Case-Specific Feature Weights , 1997, ICML.

[27]  Sang-Chan Park,et al.  Case-based reasoning and neural network based expert system for personalization , 2007, Expert Syst. Appl..

[28]  Chaochang Chiu,et al.  Predicting information systems outsourcing success using a hierarchical design of case-based reasoning , 2004, Expert Syst. Appl..

[29]  Roger C. Schank,et al.  SCRIPTS, PLANS, GOALS, AND UNDERSTANDING , 1988 .

[30]  Ingoo Han,et al.  Hybrid genetic algorithms and support vector machines for bankruptcy prediction , 2006, Expert Syst. Appl..

[31]  Uri Lipowezky Selection of the optimal prototype subset for 1-NN classification , 1998, Pattern Recognit. Lett..

[32]  Young-Chan Lee,et al.  Application of support vector machines to corporate credit rating prediction , 2007, Expert Syst. Appl..

[33]  Claire Cardie,et al.  Using Decision Trees to Improve Case-Based Learning , 1993, ICML.