Algorithms for clustering data
Algorithms for clustering 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)
Interactive exploration of very large relational datasets through 3D dynamic projections
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Geometric methods and applications: for computer science and engineering
Geometric methods and applications: for computer science and engineering
Visualizing multi-dimensional clusters, trends, and outliers using star coordinates
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Cluster validity methods: part I
ACM SIGMOD Record
HD-Eye: Visual Mining of High-Dimensional Data
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
Cluster rendering of skewed datasets via visualization
Proceedings of the 2003 ACM symposium on Applied computing
ClusterMap: labeling clusters in large datasets via visualization
Proceedings of the thirteenth ACM international conference on Information and knowledge management
VISTA: validating and refining clusters via visualization
Information Visualization
Validation and interpretation of Web users' sessions clusters
Information Processing and Management: an International Journal
Relational visual cluster validity (RVCV)
Pattern Recognition Letters
Model-Based cluster analysis for web users sessions
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
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The automatic clustering algorithms are known towork well in dealing with clusters of regular shapes, e.g.compact spherical/elongated shapes, but may incur highererror rates when dealing with arbitrarily shaped clusters.Although some efforts have been devoted to addressingthe problem of skewed datasets, the problem of handlingclusters with irregular shapes is still in its infancy,especially in terms of dimensionality of the datasets andthe precision of the clustering results considered. Notsurprisingly, the statistical indices works ineffective invalidating clusters of irregular shapes, too. In this paper,we address the problem of clustering and validatingarbitrarily shaped clusters with a visual framework(VISTA). The main idea of the VISTA approach is tocapitalize on the power of visualization and interactivefeedbacks to encourage domain experts to participate inthe clustering revision and clustering validation process.