Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
A critical review of multi-objective optimization in data mining: a position paper
ACM SIGKDD Explorations Newsletter
Form Invariance and Implicit Parallelism
Evolutionary Computation
Segmenting Customers from Population to Individuals: Does 1-to-1 Keep Your Customers Forever?
IEEE Transactions on Knowledge and Data Engineering
Multi-Objective Evolutionary Clustering using Variable-Length Real Jumping Genes Genetic Algorithm
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Fitting finite mixtures of generalized linear regressions in R
Computational Statistics & Data Analysis
Comparing parameter tuning methods for evolutionary algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Multiobjective data clustering
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
A unified framework for market segmentation and its applications
Expert Systems with Applications: An International Journal
Category role aided market segmentation approach to convenience store chain category management
Decision Support Systems
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Market segmentation is inherently a multicriterion problem even though it has often been modeled as a single-criterion problem in the traditional marketing literature and in practice. This paper discusses the multicriterion nature of market segmentation and develops a new mathematical model that addresses this issue. A new method for market segmentation based on multiobjective evolutionary algorithms, called MMSEA, is developed. It complements existing segmentation methods by optimizing multiple objectives simultaneously, searching for globally optimal solutions, and approximating a set of Pareto-optimal solutions. We have applied and evaluated this method in two empirical studies for two firms from distinct industries: descriptive segmentation of the cell phone service market from a dual-value creation perspective and predictive segmentation of retail customers based on profit and customer sociodemographic attributes. The results provide decision makers with compelling alternatives and enhanced flexibility currently missing in existing market segmentation methods.