We present a new approach to inferring a probability distribution which is incompletely specified by a number of linear constraints. We argue that the currently most popular appro...
We study methods to initialize or bias different clustering methods using prior information about the "importance" of a keyword w.r.t. the whole document collection or a...
The objective of this work is to interpret inductive results obtained by the unsupervised learning method OSHAM. We briefly introduce the learning process of OSHAM, that extracts ...
In this paper, we study the problem of applying data mining to facilitate the investigation of money laundering crimes (MLCs). We have identified a new paradigm of problems --- th...
Zhongfei (Mark) Zhang, John J. Salerno, Philip S. ...
The basic idea to defend in this paper is that an adequate perception of the search space, sacrificing most of the precision, can paradoxically accelerate the discovery of the mo...