Related sets of data items are found by first discovering the asscociation rules which are typically used in data mining applications on transactional databases to measure the affinity among items appearing in transactions. In this model, each hyperedge is weighted by the average confidence of all association rules among items appearing within the edge. The Semantic Partioning method uses a recursive bisection of data according to the principal component. We find the principal component by calculating the leading singular value of the scaled data and bisecting the data with hyperplane normal to the principal component passing through the mean. This produces a binary tree of clusters.
Computationally efficiency is achieved since only one singular value is computed per partition.
Our experiments suggest that our clustering methods performed much better and produced better quality clusters mainly because they are not sensitive to the dimensionality of the data sets, and they are linearly scalable w.r.t. There were also dramatic differences in run times of the four methods. Results of the entropy comparison of methods for one data set is depicted in Figure 3. Experimental results comparing Hypergraph Partitioning and Semantic Partitioning methods to Hierarchical Agglomerative Clustering and AutoClass. Note that a lower entropy represents better cohesion in the discovered document clusters.