Mining Multidimensional Data Using Clustering Techniques

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.