Information visualization of attributed relational data
APVis '01 Proceedings of the 2001 Asia-Pacific symposium on Information visualisation - Volume 9
Cross-relational clustering with user's guidance
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
A New Clustering Algorithm of Large Datasets with O(N) Computational Complexity
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Probabilistic classification and clustering in relational data
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Measuring constraint-set utility for partitional clustering algorithms
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
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Data clustering is essential problem in database technology – successful solutions in this field provide data storing and accessing optimizations, which yield better performance characteristics. Another advantage of clustering is in relation with ability to distinguish similar data patterns and semantically interconnected entities. This in turn is very valuable for data mining and knowledge discovery activities. Although many general clustering strategies and algorithms were developed in past years, this search is still far from end, as there are many potential implementation fields, each stating its own unique requirements. This paper describes data clustering based on original spatial partitioning of force-based graph layout, which provides natural way for data organization in relational databases. Practical usage of developed approach is demonstrated.