Computer graphics (2nd ed. in C): principles and practice
Computer graphics (2nd ed. in C): principles and practice
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Information visualization in data mining and knowledge discovery
Information visualization in data mining and knowledge discovery
Visual Data Mining: Techniques and Tools for Data Visualization and Mining
Visual Data Mining: Techniques and Tools for Data Visualization and Mining
Information Visualization and Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
Diffraction-specific fringe computation for electro-holography
Diffraction-specific fringe computation for electro-holography
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Exploratory visualization using bracketing
Proceedings of the working conference on Advanced visual interfaces
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
SCHISM: A New Approach for Interesting Subspace Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Visual and Spatial Analysis
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A Generic Framework for Efficient Subspace Clustering of High-Dimensional Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Challenges in Visual Data Analysis
IV '06 Proceedings of the conference on Information Visualization
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Clicks: An effective algorithm for mining subspace clusters in categorical datasets
Data & Knowledge Engineering
Spatial Multidimensional Sequence Clustering
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
DUSC: Dimensionality Unbiased Subspace Clustering
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
From visual data exploration to visual data mining: a survey
IEEE Transactions on Visualization and Computer Graphics
Morpheus: interactive exploration of subspace clustering
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
JSNVA: A Java Straight-Line Drawing Framework for Network Visual Analysis
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
ACM Transactions on Knowledge Discovery from Data (TKDD)
Heidi matrix: nearest neighbor driven high dimensional data visualization
Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration
Visually driven analysis of movement data by progressive clustering
Information Visualization
SOREX: subspace outlier ranking exploration toolkit
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
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To gain insight into today's large data resources, data mining extracts interesting patterns. To generate knowledge from patterns and benefit from human cognitive abilities, meaningful visualization of patterns are crucial. Clustering is a data mining technique that aims at grouping data to patterns based on mutual (dis)similarity. For high dimensional data, subspace clustering searches patterns in any subspace of the attributes as patterns are typically obscured by many irrelevant attributes in the full space. For visual analysis of subspace clusters, their comparability has to be ensured. Existing subspace clustering approaches, however, lack interactive visualization and show bias with respect to the dimensionality of subspaces. In this work, dimensionality unbiased subspace clustering and a novel distance function for subspace clusters are proposed. We suggest two visualization techniques that allow users to browse the entire subspace clustering, to zoom into individual objects, and to analyze subspace cluster characteristics in-depth. Bracketing of different parameter settings enable users to immediately see the effect of parameters on their data and hence to choose the best clustering result for further analysis. Usage of user analysis for feedback to the subspace clustering algorithm directly improves the subspace clustering. We demonstrate our visualization techniques on real world data and confirm results through additional accuracy measurements and comparison with existing subspace clustering algorithms.