'Larger data sets' presentation slideshows

Clustering

Clustering. Wei Wang. Outline. What is clustering Partitioning methods Hierarchical methods Density-based methods Grid-based methods Model-based clustering methods Outlier analysis . Outliers. Cluster 1. Cluster 2. What Is Clustering?. Group data into clusters

380 views • 27 slides

Light Field Compression Using 2-D Warping and Block Matching

Light Field Compression Using 2-D Warping and Block Matching. Shinjini Kundu Anand Kamat Tarcar EE398A Final Project. Outline. Motivation and Goals Overview of Our Method Results and Analysis Summary Future Work References. Motivation.

377 views • 27 slides

Clustering

Clustering. COMP 290-90 Research Seminar GNET 214 BCB Module Spring 2006 Wei Wang. Outline. What is clustering Partitioning methods Hierarchical methods Density-based methods Grid-based methods Model-based clustering methods Outlier analysis. Outliers. Cluster 1. Cluster 2.

474 views • 27 slides

Clustering

Clustering. COMP 790-90 Research Seminar BCB 713 Module Spring 2011 Wei Wang. Outline. What is clustering Partitioning methods Hierarchical methods Density-based methods Grid-based methods Model-based clustering methods Outlier analysis. Outliers. Cluster 1. Cluster 2.

388 views • 27 slides

Clustering

Clustering. Wei Wang. Outline. What is clustering Partitioning methods Hierarchical methods Density-based methods Grid-based methods Model-based clustering methods Outlier analysis. Outliers. Cluster 1. Cluster 2. What Is Clustering?. Group data into clusters

463 views • 27 slides

Painting on Movable Objects

Dynamic Shader Lamps. Painting on Movable Objects. Deepak Bandyopadhyay, UNC-CH Ramesh Raskar, MERL Henry Fuchs, UNC-CH. Dynamic Shader Lamps. Introduction Related Work Implementation Results. Motivation.

370 views • 26 slides

Random Forests

Random Forests. Paper presentation for CSI5388 PENGCHENG XI Mar. 23, 2005. Reference. Leo Breiman, Random Forests , Machine Learning, 45, 5-32, 2001 Leo Breiman (Professor Emeritus at UCB) is a member of the National Academy of Sciences. Abstract.

1.12k views • 23 slides

Physics Potential of an E xteded Tevatron Run

Physics Potential of an E xteded Tevatron Run. Ursula Bassler on behalf of the D0 and CDF collaborations Irfu /SPP CEA- Saclay. Thanks in particular to Darian Wood for his HCP slides!. Overview. The Context: Tevatron, CDF & Dzero , LHC Physics opportunities

340 views • 23 slides

BINF6201/8201 Molecular phylogenetic methods 4 11-10-2011

BINF6201/8201 Molecular phylogenetic methods 4 11-10-2011. Maximum likelihood methods. So far we have only considered a single site (configuration). The likelihood for all sites is the product of the likelihoods for each site if all the sites evolve independently. .

356 views • 21 slides

Plane Spotting 3 Or Opening Emergency Exit Doors at Inappropriate Times since 1985.

Plane Spotting 3 Or Opening Emergency Exit Doors at Inappropriate Times since 1985. Predicting Air Delays at SeaTac airport. A CS 773C Project by Sam Delaney. The Problem!. A Review!.

294 views • 20 slides

Gene Clustering Using Self-Organizing Maps and Particle Swarm Optimization

Gene Clustering Using Self-Organizing Maps and Particle Swarm Optimization. R. Earl Lewis, Jr. CMSC 838 Presentation. Talk Overview. Gene Clustering Using Self-Organizing Maps and Particle Swarm Optimization Authors: Xiang Xiao, Ernst Dow, Russell Eberhart,

382 views • 16 slides

DATA ANALYTICS I

386 views • 14 slides

David Le CS257, ID: 126 Feb 28, 2013

15.9 Query Execution Summary. David Le CS257, ID: 126 Feb 28, 2013. Overview. Query Processing Outline of Query Compilation Table Scanning Cost Measures Review of Algorithms One- pass Methods Nested - Loop Join Two - pass Sort- based Hash- based Index- based Multi- pass.

264 views • 14 slides

An Introduction to HDInsight

An Introduction to HDInsight. June 27 th , 2013 cschmidt@pragmaticworks.com @ sqlbischmidt http:// intelligentsql.wordpress.com. Big Data. Structured or Unstructured?. Structured data is identifiable Organized by columns and rows databases

323 views • 12 slides

220 views • 11 slides

Using Data in Undergraduate Science Classrooms

Using Data in Undergraduate Science Classrooms. Cathy Manduca and David Mogk NSDL Annual Meeting 2003. Faculty Using Data. What do we know about how faculty use data in their classes? What are the implications for NSDL and its projects?. NSDL Workshop Report. What do we mean by data?

239 views • 10 slides

Transitioning from Algorithms to Software

Transitioning from Algorithms to Software. Thomas Kue Southern Arkansas University Dr. Ernst Leiss University of Houston REU Summer 2011. Outline. VMM and the Memory Hierarchy Problem Example: Adding Matrices A Basic Algorithm Transitioning Into Software Problems in the Transition

210 views • 9 slides

Chapters 10-11, Graphical Displays

Chapters 10-11, Graphical Displays. The distribution of a variable describes what values the variable takes and how often each value occurs. (p. 175) The frequency of any value of a variable is the number of times that value occurs in the data.

167 views • 8 slides

Acknowledgement

Automatic Chamber M easurements of Methane and Carbon Dioxide Fluxes and I sotopologues of CH 4 in a sub-Arctic M ire Ryan D. Lawrence 1* , Carmody K. McCalley 2 , Patrick M. Crill 3 , Ruth K. Varner 4 , Scott R. Saleska 2

127 views • 1 slides

Cloud Computing: How to Have a Smart Conversation with IT

Cloud Computing: How to Have a Smart Conversation with IT. April 26, 2011. Presenters. Our Panel includes: Frank Lynch , Senior Counsel, Mondial Assistance Chris Burroughs , Vice President of IT Infrastructure Services, Mondial Assistance

69 views • 0 slides