Algorithms for clustering data
Algorithms for clustering data
ACM Computing Surveys (CSUR)
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
A unified framework for model-based clustering
The Journal of Machine Learning Research
An intelligent market segmentation system using k-means and particle swarm optimization
Expert Systems with Applications: An International Journal
NP-hardness of Euclidean sum-of-squares clustering
Machine Learning
Application of a 3NN+1 based CBR system to segmentation of the notebook computers market
Expert Systems with Applications: An International Journal
A two-stage clustering approach for multi-region segmentation
Expert Systems with Applications: An International Journal
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
Category role aided market segmentation approach to convenience store chain category management
Decision Support Systems
| Hi-index | 12.05 |
Market segmentation is a core marketing concept that is conceptually simple to define and understand, but inherently a multi-criteria problem that is hard to measure and computationally difficult in many aspects. This paper reviews the development of market segmentation techniques and identifies the computational issues of the applications of market segmentation. A multidimensional unified framework for market segmentation is proposed based on the relationship among segmentation variables, data measures, and the multi-objective optimization techniques implemented. We conduct an empirical comparison of two prominent methods: a concomitant finite mixture model and a multi-objective evolutionary algorithm. The result shows that the proposed framework helps to understand different segmentation models and solutions and to guide the development of new market segmentation solution techniques.