'Data space' diaporamas de présentation

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Clustering

Clustering. COMP 790-90 Research Seminar BCB 713 Module Spring 2011 Wei Wang. Drawback of Distance-based Methods. Hard to find clusters with irregular shapes Hard to specify the number of clusters Heuristic: a cluster must be dense. p. q. Directly Density Reachable.

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Chapter 2 Assembly Language Programming

Chapter 2 Assembly Language Programming. Assembly Language Programming. ? Assembly Language Programming ? ??? ?? ? ????? ?? ??? ?? ?? . Programmable Digital System ??? ??? ?? ?? ?? ???? ?? ( ???? ?? Coding ?? ?? ) ??? ?? Higher-level Language ? ???? ??? Hardware ?? ??

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Program Performance & Asymptotic Notations

Program Performance & Asymptotic Notations. CSE, POSTECH. Program Performance. Program performance is the amount of computer memory and time needed to run a program. How is it determined? Analytically performance analysis Experimentally performance measurement. Criteria for Measurement.

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dsPIC30F4011

dsPIC30F4011. Main Features. High-Performance, Modified RISC CPU: Modified Harvard architecture C compiler optimized instruction set architecture with flexible addressing modes 83 base instructions 24-bit wide instructions, 16-bit wide data path

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Unified Viewer of Map Images/Data Simplest possible map viewer

Unified Viewer of Map Images/Data Simplest possible map viewer. List of Layers. Y. TOMS. N. Model. N. Elevation. Time Selector. Year: 1998 Month: 11 Day: 12 Hour: 06. LatMin: -30. LatMax: 40. LonMin: -90. LatMax: 20.

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An Information-theoretic Framework for Visualization

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Clustering and Indexing in High-dimensional spaces

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Coupler Design Issues from the Modular Ocean Data Assimilation Project

Coupler Design Issues from the Modular Ocean Data Assimilation Project. Dr. Richard Loft Computational Science Section Scientific Computing Division National Center for Atmospheric Research Boulder, CO USA. Outline. Project Introduction goals / pariticipants / scope http//:iom.asu.edu

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A First Attempt towards a Logical Model for the PBMS

Pa tterns for N ext-Generation Da tabase Systems PANDA. A First Attempt towards a Logical Model for the PBMS. PANDA Meeting, Milano , 18 April 2002 National Technical University of Athens. Overview. General Understanding of the PBMS Mathematical Background

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Notes on our audience

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Extending metric multidimensional scaling with Bregman divergences

Extending metric multidimensional scaling with Bregman divergences. Mr. Jigang Sun Supervisor: Prof. Colin Fyfe Nov 2009. Multidimensional Scaling(MDS).

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Anonymity - Background

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SkyTEM at the extended Test site

Validating SkyTEM Data Against Ground-based TEM Data at the Danish National Test Site by Upward Continuation. Nikolaj Foged*, A.V. Christiansen**, and Esben Auken* *HydroGeophysics Group, Department of Earth Sciences, Aarhus University, Denmark

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Chapter 4 Clustering

Chapter 4 Clustering . Neural Network Based Approaches. Figure 5.12 Single artificial neuron with three inputs. Table 5.4 All possible input patterns from Figure 5.12. Finding the output value. Finding the Output Value. w 1=2 w 2=-4 w 3=1. Table 5.5

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Data Mining Techniques

Data Mining Techniques. DM is not so much a single technique, as the idea that there is more knowledge hidden in the data than shows itself on the surface. Any data that helps extract more out of data is useful. So DM techniques form quite a heterogeneous group. DM Techniques. Query tools

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XML: text format

XML: text format. Dr Andy Evans. Text-based data formats. As data space has become cheaper, people have moved away from binary data formats. Text easier to understand for humans / coders. Move to open data formats encourages text.

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Anatomy of a Wrapper Service: TOMS Satellite Image Data

Anatomy of a Wrapper Service: TOMS Satellite Image Data. The daily TOMS images reside on the FTP archive, e.g. ftp://toms.gsfc.nasa.gov/pub/eptoms/images/aerosol/y2000/ea000820.gif URL template: ftp://toms.gsfc.nasa.gov/pub/eptoms/images/aerosol/y[yyyy]/ea[yy][mm][dd].gif.

259 views • 11 slides