Visualizing multi-dimensional clusters, trends, and outliers using star coordinates
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Visualizing Data
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
iVIBRATE: Interactive visualization-based framework for clustering large datasets
ACM Transactions on Information Systems (TOIS)
ALDAI: active learning documents annotation interface
Proceedings of the 2006 ACM symposium on Document engineering
BoostCluster: boosting clustering by pairwise constraints
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Enhancing semi-supervised clustering: a feature projection perspective
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
DClusterE: A Framework for Evaluating and Understanding Document Clustering Using Visualization
ACM Transactions on Intelligent Systems and Technology (TIST)
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In this paper we propose a method that combines the advanced data analysis of the automatic statistical methods and the flexibility and manual parameter tuning of interactive visual clustering. We present the Semi-Supervised Visual Clustering (SSVC) interface; its main contribution is the learning of the optimal projection distance metric for the star and spherical coordinate visualization systems. Beyond the conventional manual setting, it couples the visual clustering with the automatic setting where the projection distance metric is learned from the available set of user feedbacks in the form of either item similarities or direct item annotations. Moreover, SSVC interface allows for the hybrid setting where some parameters are manually set by the user while the remaining parameters are determined by the optimal distance algorithm.