We develop a framework for learning generic, expressive image priors that capture the statistics of natural scenes and can be used for a variety of machine vision tasks. The appro...
Classification of large datasets is an important data mining problem. Many classification algorithms have been proposed in the literature, but studies have shown that so far no al...
Johannes Gehrke, Raghu Ramakrishnan, Venkatesh Gan...
We present D-HOTM, a framework for Distributed Higher Order Text Mining based on named entities extracted from textual data that are stored in distributed relational databases. Unl...
This paper provides a framework for the extraction of frequent sequences satisfying a given regular expression (RE) constraint. We take advantage of the information contained in th...
This paper introduces a Cellular Automata (CA) approach to spatiotemporal data mining (STDM). The recently increasing interest in using Genetic Algorithms and other evolutionary te...