Condensed representations of pattern collections have been recognized to be important building blocks of inductive databases, a promising theoretical framework for data mining, and...
This paper is an empirical investigation into the effectiveness of linear scaling adaptation for case-based software project effort prediction. We compare two variants of a linea...
Colin Kirsopp, Emilia Mendes, Rahul Premraj, Marti...
Consensus clustering is the problem of reconciling clustering information about the same data set coming from different sources or from different runs of the same algorithm. Cast ...
Clustering is often formulated as the maximum likelihood estimation of a mixture model that explains the data. The EM algorithm widely used to solve the resulting optimization pro...
We study the problem of hierarchical classification when labels corresponding to partial and/or multiple paths in the underlying taxonomy are allowed. We introduce a new hierarchi...