'Single cluster' diaporamas de présentation

Clustering

635 views • 54 slides

DATA MINING WITH CLUSTERING AND CLASSIFICATION

DATA MINING WITH CLUSTERING AND CLASSIFICATION. Spring 2007, SJSU Benjamin Lam. Overview. Definition of Clustering Existing clustering methods Clustering examples Classification Classification examples Conclusion. Definition.

690 views • 49 slides

DATA MINING WITH CLUSTERING AND CLASSIFICATION

DATA MINING WITH CLUSTERING AND CLASSIFICATION. Spring 2007, SJSU Benjamin Lam. Overview. Definition of Clustering Existing clustering methods Clustering examples Classification Classification examples Conclusion. Definition.

669 views • 49 slides

Ground Truth Free Evaluation of Segment Based Maps

Rolf Lakaemper Temple University, Philadelphia,PA,USA. Ground Truth Free Evaluation of Segment Based Maps. An example for evaluation of a robot map based on mid level geometry (and an advertisement for segment based maps)

462 views • 46 slides

Discrimination and Classification

Discrimination and Classification. Discrimination. Situation: We have two or more populations p 1 , p 2 , etc (possibly p -variate normal). The populations are known (or we have data from each population)

456 views • 45 slides

A first look at the Gigatracker simulation

A first look at the Gigatracker simulation. Massimiliano Fiorini CERN. NA62 Gigatracker Working Group 28 July 2009. Software. NA62 MonteCarlo fast simulation of the beam line based on Flyo called by Geant4 to generate primary particles

374 views • 29 slides

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pd ???????????????. K. Sagara 1 , Y. Tameshige 2 , K. Hatanaka 2 , S. Kuroita 1 , H. Matsubara 2 , K. Fujita1, Y. Shimizu 2 , T. Kawabata 3 , Y. Maeda 5 , Y. Sakemi, H. Okamura 2 , K. Sekiguchi 4 , Tamii 2 , and T. Wakasa 1 1 Dept. of Physics, Kyushu University 2 RCNP, Osaka University

1.26k views • 28 slides

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pd ???????????????. K. Sagara 1 , Y. Tameshige 2 , K. Hatanaka 2 , S. Kuroita 1 , H. Matsubara 2 , K. Fujita1, Y. Shimizu 2 , T. Kawabata 3 , Y. Maeda 5 , Y. Sakemi, H. Okamura 2 , K. Sekiguchi 4 , Tamii 2 , and T. Wakasa 1 1 Dept. of Physics, Kyushu University 2 RCNP, Osaka University

338 views • 28 slides

An Adaptive File Distribution Algorithm for Wide Area Network

An Adaptive File Distribution Algorithm for Wide Area Network Takashi Hoshino, Kenjiro Taura, Takashi Chikayama University of Tokyo. Background. New environments for parallel and distributed computation Clusters, cluster of clusters, GRID Offer scalability and good cost performance

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CLUSTERING

CLUSTERING. Overview. Definition of Clustering Existing clustering methods Clustering examples. Definition.

364 views • 27 slides

354 views • 23 slides

A Multi-Site Virtual Cluster System for Wide Area Networks

A Multi-Site Virtual Cluster System for Wide Area Networks. Takahiro Hirofuchi, Takeshi Yokoi, Tadashi Ebara, Yusuke Tanimura, Hirotaka Ogawa, Hidetomo Nakada, Yoshio Tanaka, and Satoshi Sekiguchi National Institute of Advanced Industrial Science and Technology (AIST). What is Virtual Cluster?.

303 views • 23 slides

CAP5415 Computer Vision Spring 2003

CAP5415 Computer Vision Spring 2003. Khurram Hassan-Shafique. Region Segmentation. Region Segmentation. Find sets of pixels, such that All pixels in region i satisfy some constraint of similarity. Choose a fixed number of clusters

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1 Samuel 17

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Column Oriented Database Vs Row Oriented Databases

Column Oriented Database Vs Row Oriented Databases. By Rakesh Venkat. Index. Introduction- Column Oriented Databases List of Column Oriented Databases Pros and Cons MonetDB Performance Analysis LucidDB Performance Analysis Conclusion. Introduction.

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Experiences of Grid Enabled MPI Implementation named MPICH-GX with Atmospheric Applications

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Problem Definition

GSM. PCM. MPEG-3. PCM. GSM. Problem Definition. ICEBERG Call Session. Data path Created by the Automatic Path Creation (APC) component Service: program with well-defined interface Operator: stateless service instance Path: composition of operators. Control signaling

231 views • 9 slides

Marc Fiuczynski Princeton University Marco Yuen University of Victoria

PlanetLab & Clusters. Marc Fiuczynski Princeton University Marco Yuen University of Victoria. Benefits of Clusters to PlanetLab. CYCLES: more compute resources for public PlanetLab Easier to convince folks to commit machines on a short term basis vs. permanently to PlanetLab

266 views • 8 slides