Modeling Cognitive Development on Balance Scale Phenomena
Machine Learning - Special issue on computational models of human learning
ACM Computing Surveys (CSUR)
Self-Organizing Maps
Techniques of Cluster Algorithms in Data Mining
Data Mining and Knowledge Discovery
Data Mining by Means of Binary Representation: A Model for Similarity and Clustering
Information Systems Frontiers
Model-Based Clustering and Visualization of Navigation Patterns on a Web Site
Data Mining and Knowledge Discovery
A rank-by-feature framework for interactive exploration of multidimensional data
Information Visualization
Factor-analysis based anomaly detection and clustering
Decision Support Systems
A hybrid sales forecasting system based on clustering and decision trees
Decision Support Systems
A multicriteria decision support system for housing evaluation
Decision Support Systems
Investigating diversity of clustering methods: An empirical comparison
Data & Knowledge Engineering
Weighted order-dependent clustering and visualization of web navigation patterns
Decision Support Systems
Application of complex adaptive systems to pricing of reproducible information goods
Decision Support Systems
Visual interactive evolutionary algorithm for high dimensional data clustering and outlier detection
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
From visual data exploration to visual data mining: a survey
IEEE Transactions on Visualization and Computer Graphics
Density link-based methods for clustering web pages
Decision Support Systems
Towards supporting expert evaluation of clustering results using a data mining process model
Information Sciences: an International Journal
Ranking and selection of unsupervised learning marketing segmentation
Knowledge-Based Systems
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Clustering decisions frequently arise in business applications such as recommendations concerning products, markets, human resources, etc. Currently, decision makers must analyze diverse algorithms and parameters on an individual basis in order to establish preferences on the decision-making issues they face; because there is no supportive model or tool which enables comparing different result-clusters generated by these algorithms and parameters combinations. The Multi-Algorithm-Voting (MAV) methodology enables not only visualization of results produced by diverse clustering algorithms, but also provides quantitative analysis of the results. The current research applies MAV methodology to the case of recommending new-car pricing. The findings illustrate the impact and the benefits of such decision support system.