Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Data mining solutions: methods and tools for solving real-world problems
Data mining solutions: methods and tools for solving real-world problems
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)
Visualizing multi-dimensional clusters, trends, and outliers using star coordinates
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Self-Organizing Maps
A Visual Method of Cluster Validation with Fastmap
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
An Empirical Study on the Visual Cluster Validation Method with Fastmap
DASFAA '01 Proceedings of the 7th International Conference on Database Systems for Advanced Applications
Inventing Discovery Tools: Combining Information Visualization with Data Mining
DS '01 Proceedings of the 4th International Conference on Discovery Science
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
VISTA: validating and refining clusters via visualization
Information Visualization
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
HOV3: an approach to visual cluster analysis
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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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Visualization is helpful for clustering high dimensional data. The goals of visualization in data mining are exploration, confirmation and presentation of the clustering results. However, the most of visual techniques developed for cluster analysis are primarily focused on cluster presentation rather than cluster exploration. Several techniques have been proposed to explore cluster information by visualization, but most of them depend heavily on the individual user's experience. Inevitably, this incurs subjectivity and randomness in the clustering process. In this paper, we employ the statistical features of datasets as predictions to estimate the number of clusters by a visual technique called HOV3. This approach mitigates the problem of the randomness and subjectivity of the user during the process of cluster exploration by other visual techniques. As a result, our approach provides an effective visual method for cluster exploration.