Decision tree induction algorithms scale well to large datasets for their univariate and divide-and-conquer approach. However, they may fail in discovering effective knowledge when...
Giovanni Giuffrida, Wesley W. Chu, Dominique M. Ha...
-- Combination of multiple clusterings is an important task in the area of unsupervised learning. Inspired by the success of supervised bagging algorithms, we propose a resampling ...
Behrouz Minaei-Bidgoli, Alexander P. Topchy, Willi...
Dyadic data matrices, such as co-occurrence matrix, rating matrix, and proximity matrix, arise frequently in various important applications. A fundamental problem in dyadic data a...
The problem of similarity search (query-by-content) has attracted much research interest. It is a difficult problem because of the inherently high dimensionality of the data. The ...
Discovering association rules that identify relationships among sets of items is an important problem in data mining. Finding frequent item sets is computationally the most expens...