To successfully prepare and model data, the data miner needs to be aware of the properties of the data manifold. In this chapter, the outline of a tool for automatically generating...
Abstract. Novelty detection in data stream mining denotes the identification of new or unknown situations in a stream of data elements flowing continuously in at rapid rate. This...
The martingale framework for detecting changes in data stream, currently only applicable to labeled data, is extended here to unlabeled data using clustering concept. The one-pass...
This paper describes how to solve very large TravelingSalesman Problems heuristically by the parallelization of self-organizing maps on cluster architectures. The used way of para...
Hannes Schabauer, Erich Schikuta, Thomas Weish&aum...
The ability to identify and present the most essential aspects of time-varying data is critically important in many areas of science and engineering. This paper introduces an impor...