A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovery of decision rules in relational databases: a rough set approach
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Data mining using extensions of the rough set model
Journal of the American Society for Information Science - Special issue: knowledge discovery and data mining
Using neural networks for data mining
Future Generation Computer Systems - Special double issue on data mining
Fuzzy set technology in knowledge discovery
Fuzzy Sets and Systems
A rough set approach to attribute generalization in data mining
Information Sciences: an International Journal
Evolutionary algorithms in data mining: multi-objective performance modeling for direct marketing
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Neural networks in business: techniques and applications for the operations researcher
Computers and Operations Research - Neural networks in business
Knowledge management and data mining for marketing
Decision Support Systems - Knowledge management support of decision making
Why so many clustering algorithms: a position paper
ACM SIGKDD Explorations Newsletter
Integration of self-organizing feature map and K-means algorithm for market segmentation
Computers and Operations Research
Soft Computing and Fuzzy Logic
IEEE Software
Mining Knowledge Rules from Databases: A Rough Set Approach
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Purchase Prediction in Database Marketing with the ProbRough System
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Cluster analysis in industrial market segmentation through artificial neural network
Computers and Industrial Engineering - 26th International conference on computers and industrial engineering
An intelligent system for customer targeting: a data mining approach
Decision Support Systems
Multi-objective rule mining using genetic algorithms
Information Sciences: an International Journal - Special issue: Soft computing data mining
A new cluster validity measure and its application to image compression
Pattern Analysis & Applications
Customer Targeting: A Neural Network Approach Guided by Genetic Algorithms
Management Science
Integrating AHP and data mining for product recommendation based on customer lifetime value
Information and Management
Journal of Systems and Software
Applying knowledge engineering techniques to customer analysis in the service industry
Advanced Engineering Informatics
Clustering people according to their preference criteria
Expert Systems with Applications: An International Journal
A recommender system using GA K-means clustering in an online shopping market
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
On heuristics as a fundamental constituent of soft computing
Fuzzy Sets and Systems
Outlier identification and market segmentation using kernel-based clustering techniques
Expert Systems with Applications: An International Journal
Classifying the segmentation of customer value via RFM model and RS theory
Expert Systems with Applications: An International Journal
An intelligent market segmentation system using k-means and particle swarm optimization
Expert Systems with Applications: An International Journal
Segmentation of stock trading customers according to potential value
Expert Systems with Applications: An International Journal
Intelligent profitable customers segmentation system based on business intelligence tools
Expert Systems with Applications: An International Journal
Modeling human behavior in user-adaptive systems: Recent advances using soft computing techniques
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Customer segmentation of multiple category data in e-commerce using a soft-clustering approach
Electronic Commerce Research and Applications
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Supervised fuzzy clustering for rule extraction
IEEE Transactions on Fuzzy Systems
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
Web mining in soft computing framework: relevance, state of the art and future directions
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Segmentation has been taken immense attention and has extensively been used in strategic marketing. Vast majority of the research in this area focuses on the usage or development of different techniques. By means of the internet and database technologies, huge amount of data about markets and customers has now become available to be exploited and this enables researchers and practitioners to make use of sophisticated data analysis techniques apart from the traditional multivariate statistical tools. These sophisticated techniques are a family of either data mining or machine learning research. Recent research shows a tendency towards the usage of them into different business and marketing problems, particularly in segmentation. Soft computing, as a family of data mining techniques, has been recently started to be exploited in the area of segmentation and it stands out as a potential area that may be able to shape the future of segmentation research. In this article, the current applications of soft computing techniques in segmentation problem are reviewed based on certain critical factors including the ones related to the segmentation effectiveness that every segmentation study should take into account. The critical analysis of 42 empirical studies reveals that the usage of soft computing in segmentation problem is still in its early stages and the ability of these studies to generate knowledge may not be sufficient. Given these findings, it can be suggested that there is more to dig for in order to obtain more managerially interpretable and acceptable results in further studies. Also, recommendations are made for other potentials of soft computing in segmentation research.