Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/DEXA.2007.102 | DEXA Workshops |
Keywords | Field | DocType |
clustering techniques,greatest correlation,multidimensional data,distinct pattern set,performance view,compositional view,mining multidimensional data,hierarchical clustering,relevant association,correlated cluster,best partition,novel data mining procedure,data mining | Hierarchical clustering,Data mining,Clustering high-dimensional data,Pattern recognition,Correlation clustering,Computer science,Consensus clustering,Artificial intelligence,Biclustering,Cluster analysis,Brown clustering,Single-linkage clustering | Conference |
ISBN | Citations | PageRank |
0-7695-2932-1 | 2 | 0.41 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marco Pagani | 1 | 8 | 2.49 |
| Gloria Bordogna | 2 | 974 | 103.99 |
| Massimiliano Valle | 3 | 2 | 0.41 |