BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
A survey of visualizations for high-dimensional data mining
Information visualization in data mining and knowledge discovery
Visualizing multi-dimensional clusters, trends, and outliers using star coordinates
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Visualizing Data
VisDB: Database Exploration Using Multidimensional Visualization
IEEE Computer Graphics and Applications
Discovery Visualization Using Fast Clustering
IEEE Computer Graphics and Applications
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Inventing Discovery Tools: Combining Information Visualization with Data Mining
DS '01 Proceedings of the 4th International Conference on Discovery Science
INFOVIS '97 Proceedings of the 1997 IEEE Symposium on Information Visualization (InfoVis '97)
VIS '91 Proceedings of the 2nd conference on Visualization '91
Visualizing changes in the structure of data for exploratory feature selection
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
FAÇADE: a fast and effective approach to the discovery of dense clusters in noisy spatial data
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
VISTA: validating and refining clusters via visualization
Information Visualization
From visual data exploration to visual data mining: a survey
IEEE Transactions on Visualization and Computer Graphics
A Prediction-Based Visual Approach for Cluster Exploration and Cluster Validation by HOV3
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
A Visual Method for High-Dimensional Data Cluster Exploration
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Enhanced visual separation of clusters by M-mapping to facilitate cluster analysis
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
The discovery of hierarchical cluster structures assisted by a visualization technique
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
A top-down approach for hierarchical cluster exploration by visualization
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
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Clustering is a major technique in data mining. However the numerical feedback of clustering algorithms is difficult for user to have an intuitive overview of the dataset that they deal with. Visualization has been proven to be very helpful for high-dimensional data analysis. Therefore it is desirable to introduce visualization techniques with user’s domain knowledge into clustering process. Whereas most existing visualization techniques used in clustering are exploration oriented. Inevitably, they are mainly stochastic and subjective in nature. In this paper, we introduce an approach called HOV3 (Hypothesis Oriented Verification and Validation by Visualization), which projects high-dimensional data on the 2D space and reflects data distribution based on user hypotheses. In addition, HOV3 enables user to adjust hypotheses iteratively in order to obtain an optimized view. As a result, HOV3 provides user an efficient and effective visualization method to explore cluster information.