Mining multidimensional data using clustering techniques
M Pagani, G Bordogna, M Valle - 18th International Workshop …, 2007 - ieeexplore.ieee.org
M Pagani, G Bordogna, M Valle
18th International Workshop on Database and Expert Systems …, 2007•ieeexplore.ieee.orgWe describe a novel data mining procedure to discover relevant associations in
multidimensional data. The procedure applies hierarchical clustering to distinct pattern sets
(views) of the same dataset and identifies the best partitions in the two dendrograms that
exhibit the greatest correlation. Finally the most relevant associations between pattern sets
characterizing the most correlated clusters in the identified partitions are discovered. An
application of the procedure to identify association between compositional views and …
multidimensional data. The procedure applies hierarchical clustering to distinct pattern sets
(views) of the same dataset and identifies the best partitions in the two dendrograms that
exhibit the greatest correlation. Finally the most relevant associations between pattern sets
characterizing the most correlated clusters in the identified partitions are discovered. An
application of the procedure to identify association between compositional views and …
We describe a novel data mining procedure to discover relevant associations in multidimensional data. The procedure applies hierarchical clustering to distinct pattern sets(views) of the same dataset and identifies the best partitions in the two dendrograms that exhibit the greatest correlation.Finally the most relevant associations between pattern sets characterizing the most correlated clusters in the identified partitions are discovered. An application of the procedure to identify association between compositional views and performance views of a dataset of materials is discussed.
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