Spatial data mining is a process used to discover interesting but not explicitly available, highly usable patterns embedded in both spatial and nonspatial data, which are possibly ...
Background: The analysis of large-scale data sets via clustering techniques is utilized in a number of applications. Biclustering in particular has emerged as an important problem...
Peter A. DiMaggio Jr., Scott R. McAllister, Christ...
Abstract. Linear Discriminant (LD) techniques are typically used in pattern recognition tasks when there are many (n >> 104 ) datapoints in low-dimensional (d < 102 ) spac...
We consider the problem of distributed classification of multiple observations of the same object that are collected in an ad-hoc network of vision sensors. Assuming that each sen...
Computing statistical information on probabilistic data has attracted a lot of attention recently, as the data generated from a wide range of data sources are inherently fuzzy or ...