Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
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
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Clustering methods for large databases: from the past to the future
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Clustering techniques for large data sets—from the past to the future
KDD '99 Tutorial notes of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A Monte Carlo algorithm for fast projective clustering
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
HD-Eye: Visual Mining of High-Dimensional Data
IEEE Computer Graphics and Applications
A Distribution-Based Clustering Algorithm for Mining in Large Spatial Databases
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Parallel coordinates: a tool for visualizing multi-dimensional geometry
VIS '90 Proceedings of the 1st conference on Visualization '90
XmdvTool: integrating multiple methods for visualizing multivariate data
VIS '94 Proceedings of the conference on Visualization '94
Grid-Clustering: An Efficient Hierarchical Clustering Method for Very Large Data Sets
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The clustering of large databases is an important research area with a large variety of applications in the database context. Missing in most of the research efforts are means for guiding the clustering process and understanding the results, which is especially important for high-dimensional data. Visualization technology may help solve this problem because it provides effective support of different clustering paradigms and allows a visual inspection of the results. The HD-Eye (High-Dimensional Eye) system shows that a tight integration of advanced clustering algorithms and state-of-the-art visualization techniques is powerful enough for a better understanding and effective guidance of the clustering process, and therefore can help significantly improve the clustering results.