Fuzzy entropy and conditioning
Information Sciences: an International Journal
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A marketing category management system: a decision support system using scanner data
Decision Support Systems
Clustering of interval data based on city-block distances
Pattern Recognition Letters
Automated Variable Weighting in k-Means Type Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
An extended self-organizing map network for market segmentation: a telecommunication example
Decision Support Systems
A recommender system using GA K-means clustering in an online shopping market
Expert Systems with Applications: An International Journal
Outlier identification and market segmentation using kernel-based clustering techniques
Expert Systems with Applications: An International Journal
When is 'nearest neighbour' meaningful: A converse theorem and implications
Journal of Complexity
Spatially enabled customer segmentation using a data classification method with uncertain predicates
Decision Support Systems
Retail pricing decisions and product category competitive structure
Decision Support Systems
Application of particle swarm optimization and perceptual map to tourist market segmentation
Expert Systems with Applications: An International Journal
A unified framework for market segmentation and its applications
Expert Systems with Applications: An International Journal
Direct marketing decision support through predictive customer response modeling
Decision Support Systems
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Category management (CM) plays an increasingly important role in retailing management, as it aids retailers to increase their core competitiveness, maximise profits and ensure a good long-term customer relationship. This technique has been successfully applied to diverse large manufacturers and wholesale retailers. However, it remains a challenging task to directly employ the CM technique in convenience store (CVS) chain(s). This is because CVS chains are often distributed in a variety of areas, each store has impulsive consumers, and the traditional market segmentation attributes (e.g. consumer age, salary, and background) are difficult to collect under such circumstances. This makes it impractical to apply one general CM solution to all CVS chains. Hence, it is crucial to segment a market region and then apply customised CM solutions to the corresponding segments. This paper presents an innovative market segmentation model which is driven by category-role (CR), for the first time, to support CM in CVS chains. A new similarity measure (named HCsim()) and an improved weighted fuzzy K-means clustering algorithm (WFKM) are developed in an effort to cluster the CVSs. The usefulness and applicability of this study is illustrated by means of an empirical study to provide marketing strategy decision support. The derived results are also discussed and compared with existing methods.