Supervised learning is a classic data mining problem where one wishes to be be able to predict an output value associated with a particular input vector. We present a new twist on...
David R. Musicant, Janara M. Christensen, Jamie F....
Discovering complex associations, anomalies and patterns in distributed data sets is gaining popularity in a range of scientific, medical and business applications. Various algor...
Omer F. Rana, David W. Walker, Maozhen Li, Steven ...
Estimating missing sensor values is an inherent problem in sensor network applications; however, existing data estimation approaches do not apply well to the context of datastream...
In this work, we study the correlation between attribute sets and the occurrence of dense subgraphs in large attributed graphs, a task we call structural correlation pattern minin...
The usefulness of the results produced by data mining methods can be critically impaired by several factors such as (1) low quality of data, including errors due to contamination, ...
Fang Chu, Yizhou Wang, Carlo Zaniolo, Douglas Stot...