Titlebook: Data Mining for Scientific and Engineering Applications; Robert L. Grossman,Chandrika Kamath,Raju R. Nambur Book 2001 Springer Science+Bus

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,HDDI™: Hierarchical Distributed Dynamic Indexing, global Internet/World Wide Web exemplifies the rapid deployment of such technologies. Despite significant accomplishments in internetworking, however, scalable indexing and data-mining techniques for computational knowledge management lag behind the rapid growth of distributed collections. Hierarch

Mumble

Parallel Algorithms for Clustering High-Dimensional Large-Scale Datasets,scientific and commercial applications. Clustering is the process of identifying dense regions in a sparse multi-dimensional data set. Several clustering techniques proposed earlier either lack in scalability to a very large set of dimensions or to a large data set. Many of them require key user inp

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难解

https://doi.org/10.1007/978-3-322-89768-8 networks, graphical models, and flexible predictive modeling. The primary conclusion is that closer integration of computational methods with statistical thinking is likely to become increasingly important in data mining applications.

遭受

Comictheorie(n) und Forschungspositionen introduces HDDI™, focusing on the model building techniques employed at each node of the hierarchy. A novel approach to information clustering based on the contextual transitivity of similarity between terms is introduced. We conclude with several example applications of HDDI™ in the textual data mining and information retrieval fields.

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Understanding High Dimensional and Large Data Sets: Some Mathematical Challenges and Opportunities,rge data sets. There is a need, therefore, for new fundamental thinking about these problems and new mathematical approaches. In this paper we review a few such promising directions that draw extensively from fertile areas of harmonic analysis, discrete mathematics, stochastic analysis, and statistical methods.

悲观

Data Mining at the Interface of Computer Science and Statistics, networks, graphical models, and flexible predictive modeling. The primary conclusion is that closer integration of computational methods with statistical thinking is likely to become increasingly important in data mining applications.

ANT

,HDDI™: Hierarchical Distributed Dynamic Indexing, introduces HDDI™, focusing on the model building techniques employed at each node of the hierarchy. A novel approach to information clustering based on the contextual transitivity of similarity between terms is introduced. We conclude with several example applications of HDDI™ in the textual data mining and information retrieval fields.