A near-optimal initial seed value selection in K-means algorithm using a genetic algorithm
Pattern Recognition Letters
In search of optimal clusters using genetic algorithms
Pattern Recognition Letters
Fuzzy allocation of manufacturing resources
Proceedings of the 21st international conference on Computers and industrial engineering
Knowledge management and data mining for marketing
Decision Support Systems - Knowledge management support of decision making
Cluster center initialization algorithm for K-means clustering
Pattern Recognition Letters
A k-mean clustering algorithm for mixed numeric and categorical data
Data & Knowledge Engineering
Weighted order-dependent clustering and visualization of web navigation patterns
Decision Support Systems
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
Toward a hybrid data mining model for customer retention
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
A genetic algorithm with gene rearrangement for K-means clustering
Pattern Recognition
The capacitated centred clustering problem
Computers and Operations Research
Collaborative clustering with background knowledge
Data & Knowledge Engineering
Intelligent profitable customers segmentation system based on business intelligence tools
Expert Systems with Applications: An International Journal
Data Mining: Concepts, Models, Methods, and Algorithms
Data Mining: Concepts, Models, Methods, and Algorithms
A RFID-based Resource Allocation System for garment manufacturing
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Appropriate organizational resources allocation becomes a major challenge for companies to address the rapid demands for resources from different operational aspects while resource utilization is keeping low. Differentiate exiting customers with common features into smaller groups can serve as a piece of useful reference for decision-making. So far, k-means algorithm is the most commonly used clustering technique for conducting customer grouping. However, k-means limits the grouping consideration to a fixed number of dimensions among each group and the grouping results are significantly influenced by the initial clusters means. In this research, a robust genetic algorithm (GA) based k-means clustering algorithm is proposed in attempt to classify existing customers of the enterprise into groups with consideration of relevant attributes for the sake of obtaining desirable grouping results in an efficient manner. Different from k-means, the proposed GA-based k-means algorithm is able to select which and how many dimensions are better to be considered for each customer group when developing approximate optimal solutions. A case study is conducted on a window curtain manufacturer with the application of software Generator associated with MS Excel.