'Cluster centers' presentation slideshows

Day 2

Day 2. Agenda. Parallelism basics Parallel machines Parallelism again High Throughput Computing Finding the right grain size. One thing to remember. Easy. Hard. Seeking Concurrency. Data dependence graphs Data parallelism Functional parallelism Pipelining. Data Dependence Graph.

1.66k views • 147 slides

Lecture 9 Pattern recognition

Lecture 9 Pattern recognition. Bioinformatics Master Course Bioinformatics Data Analysis and Tools. Patterns Some are easy some are not. Knitting patterns Cooking recipes Pictures (dot plots) Colour patterns Maps Protein structures Protein sequences Protein interaction maps.

1.06k views • 85 slides

ICS 278: Data Mining Lecture 9,10: Clustering Algorithms

ICS 278: Data Mining Lecture 9,10: Clustering Algorithms. Padhraic Smyth Department of Information and Computer Science University of California, Irvine. Project Progress Report. Written Progress Report: Due Tuesday May 18 th in class

783 views • 62 slides

Lecture 21 Gene expression and the transcriptome II

Lecture 21 Gene expression and the transcriptome II. Content. SAGE mRNA abundance and function Comparing expression profiles Eisen dataset Array CGH. SAGE. SAGE = Serial Analysis of Gene Expression Based on serial sequencing of 10 to 14-bp tags that are unique to each and every gene

784 views • 60 slides

Clustering

Clustering. Rong Jin. What is Clustering?. Identify the underlying structure for given data points Doc. clustering: groups documents of same topics into the same cluster. $$$. age. query. Improve IR by Document Clustering. Cluster-based retrieval. Improve IR by Document Clustering.

807 views • 59 slides

Clustering of Non-Quantitative Data

Nanjing University of Science & Technology. Clustering of Non-Quantitative Data. Lonnie C. Ludeman New Mexico State University Las Cruces, New Mexico, USA Nov 23, 2005. Like to Thank. Nanjing University of Science & Technology. Department of Computer Science. especially.

666 views • 51 slides

Parallel Programming in C with MPI and OpenMP

Parallel Programming in C with MPI and OpenMP. Michael J. Quinn. Chapter 1. Motivation and History. Outline. Motivation Modern scientific method Evolution of supercomputing Modern parallel computers Seeking concurrency Data clustering case study Programming parallel computers.

685 views • 50 slides

CSC2515 Fall 2007 Introduction to Machine Learning Lecture 5: Mixture models, EM and variational inference

CSC2515 Fall 2007 Introduction to Machine Learning Lecture 5: Mixture models, EM and variational inference. All lecture slides will be available as .ppt, .ps, & .htm at www.cs.toronto.edu/~hinton Many of the figures are provided by Chris Bishop

695 views • 45 slides

Clustering

663 views • 42 slides

More About Clustering

More About Clustering. Naomi Altman Nov '06. Assessing Clusters. Some things we might like to do: Understand the within cluster similarity and between cluster differences in terms of distances and expression profiles. Decide how many clusters "are" in the data.

467 views • 36 slides

Fuzzy C-Means Clustering

Fuzzy C-Means Clustering. Course Project Presentation. Mahdi Amiri June 2003 Sharif University of Technology. Presentation Outline. Motivation and Goals Fuzzy C-Means Clustering (FCM) Possibilistic C-Means Clustering (PCM) Fuzzy-Possibilistic C-Means (FPCM)

642 views • 33 slides

Machine Learning CS 165B Spring 2012

Machine Learning CS 165B Spring 2012. Course outline. Introduction (Ch. 1) Concept learning (Ch. 2) Decision trees (Ch. 3) Ensemble learning Neural Networks (Ch. 4) Linear classifiers Support Vector Machines Bayesian Learning (Ch. 6 ) Genetic Algorithms (Ch. 9 )

401 views • 29 slides

Clustering with k-means and mixture of Gaussian densities

Clustering with k-means and mixture of Gaussian densities. Jakob Verbeek December 3, 2010 Course website: http://lear.inrialpes.fr/~verbeek/MLCR.10.11.php. Plan for the course. Session 1, October 1 2010 Cordelia Schmid: Introduction Jakob Verbeek: Introduction Machine Learning

494 views • 27 slides

Interactive BRDF Estimation for Mixed-Reality Applications

Interactive BRDF Estimation for Mixed-Reality Applications. Martin Knecht, Georg Tanzmeister, Christoph Traxler, Michael Wimmer Institute of Computer Graphics and Algorithms Vienna University of Technology. Motivation. Goal of our mixed reality framework

357 views • 26 slides

Analisis Cluster

Analisis Cluster. Oleh : Rahmad Wijaya. Pokok Bahasan. 1. Konsep Dasar 2. Statistik dalam Analisis Cluster 3. Langkah-langkah Analisis Cluster Rumuskan Permasalahan Memilih ukuran Jarak atau Kesamaan Memilih Prosedur Peng-clusteran Menetapkan Jumlah Cluster

1.21k views • 25 slides

Eric Papenhausen and Bing Wang (Stony Brook University) Sungsoo Ha (SUNY Korea)

GPU-Accelerated Incremental Correlation Clustering of Large Data with Visual Feedback. Eric Papenhausen and Bing Wang (Stony Brook University) Sungsoo Ha (SUNY Korea) Alla Zelenyuk (Pacific Northwest National Lab) Dan Imre ( Imre Comsulting )

360 views • 25 slides

415 views • 23 slides

In Search of Meaning for Time Series Subsequence Clustering

521 views • 22 slides

Introduction to clustering.

Introduction to clustering. by Scott Amack. What is data mining?. knowledge mining from data knowledge extraction data/pattern analysis knowledge discovery from data. Why is data mining important?. Data mining helps us to find interesting patterns that we may not have noticed otherwise.

426 views • 21 slides

Computational model of the brain stem functions

Computational model of the brain stem functions. W?odzis?aw Duch , Krzysztof Dobosz, Grzegorz Osi?ski Department of Informatics /Physics Nicolaus Copernicus University , Toru?, Poland Google: W. Duch Neuromath , Rome , Dec. 2007. Brain stem.

260 views • 13 slides