CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Visualizing multi-dimensional clusters, trends, and outliers using star coordinates
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
HD-Eye: Visual Mining of High-Dimensional Data
IEEE Computer Graphics and Applications
Human Factors in Visualization Research
IEEE Transactions on Visualization and Computer Graphics
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
VISTA: validating and refining clusters via visualization
Information Visualization
iVIBRATE: Interactive visualization-based framework for clustering large datasets
ACM Transactions on Information Systems (TOIS)
Proceedings of the 12th international conference on Intelligent user interfaces
A filter feature selection method for clustering
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
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Interactive visual clustering allows the user to be involved into the clustering through visualizing process via interactive visualization. In order to perform effective interaction in the visual clustering process, the efficient feature selection methods are required to identify the most dominating features. Hence, in this paper an improved visual clustering system is proposed using an efficient feature selection method. The relevant features for visual clustering are identified based on their contribution to the entropy. Experimental results show that the proposed method works well in finding the best cluster.