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

夾克怕包裹

Mining Astronomical Databases, We briefly describe some of the problems astronomical data and datasets present and give an example from our own efforts to auto- mate the classification of galaxies, and then discuss where “clustering” algorithms may be applicable.

Obituary

Searching for Bent-Double Galaxies in the First Survey, this chapter, we describe our experiences in applying data mining to a problem in astronomy, namely, the identification of radio-emitting galaxies with a bent-double morphology. Until recently, astronomers associated with the FIRST (Faint images of the radio Sky at Twenty-cm) survey identified thes

幼稚

Data Mining Applications in Bioinformatics, bioinformatics and the motivating data management and analysis tasks. Descriptions of successful applications are given, along with an outline of the near-future potential and issues affecting the successful application of data mining.

學(xué)術(shù)討論會(huì)

Mining Residue Contacts in Proteins,leation-propagation model of protein folding. We apply a hybrid approach, using a Hidden Markov Model to extract folding initiation sites, and then apply association mining to discover contact potentials. The new hybrid approach achieves accuracy results better than those reported previously.

ANTI

KDD Services at the Goddard Earth Sciences Distributed Active Archive Center,te sensing satellites. End users of the data range from instrument scientists to global change and climate researchers to federal agencies and foreign governments. Many of these users apply Knowledge Discovery from Databases (KDD) techniques to large volumes of data (on the order of a terabyte) rece

動(dòng)物

pacific

新娘

Real Time Feature Extraction for the Analysis of Turbulent Flows,mber of applications. In recent times with the advent of supercomputers and new experimental imaging techniques, terabyte scale data sets are being generated, and hence storage as well as analysis of this data has become a major issue. In this chapter we outline a new approach to tackling these data

ABHOR

Data Mining for Turbulent Flows,p engineers and scientists unravel the causal relationships in the underlying system. In this chapter, we propose several data modeling methods to incorporate spatial and temporal features of scientific simulation data and investigate some of them in the context of developing models for predicting b

Interregnum

,EVITA — Efficient Visualization and Interrogation of Tera-Scale Data,nt of appropriate data management and visualization techniques has not kept pace with the growth in size and complexity of such datasets. To address these issues, we are developing a prototype, integrated system (EVITA) to facilitate exploration of tera-scale datasets. The cornerstone of the EVITA s