'Clustering results' diaporamas de présentation

Cluster Analysis

Cluster Analysis. Mark Stamp. Cluster Analysis. Grouping objects in meaningful way Clustered data fits together in some way Can help to make sense of (big) data Useful technique in many fields Many different clustering strategies Overview, then details on 2 methods

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Tight Clustering: a method for extracting stable and tight patterns in expression profiles

Tight Clustering: a method for extracting stable and tight patterns in expression profiles. A class of penalized and weighted K-means for clustering. George C. Tseng Dept. of Biostatistics & Human Genetics University of Pittsburgh. Image analysis. Normalization. Identify differentially

793 views • 52 slides

AMCS/CS 340: Data Mining

Clustering. AMCS/CS 340: Data Mining. Xiangliang Zhang King Abdullah University of Science and Technology. Grouping fruits. Grouping apple with apple , orange with orange and banana with banana. 2. Give pictures to a computer. 3. C hange pictures to data. 4.

795 views • 49 slides

10/05/2002

The Search Landscape of Graph Partitioning Problems using Coupling and Cohesion as the Clustering Criteria. Brian S. Mitchell & Spiros Mancoridis {bmitchel,smancori}@mcs.drexel.edu http://www.mcs.drexel.edu/~{bmitchel,smancori} Department of Computer Science

551 views • 46 slides

A System for Outlier Detection and Cluster Repair

A System for Outlier Detection and Cluster Repair. Ying Liu Dr. Sprague Oct 21, 2005. A data set. Clustering algorithms could generate bad cluster. hMETIS (k=6). Clustering algorithms could generate bad cluster. hMETIS (k=20). BIRCH. BIRCH.

537 views • 37 slides

Applications

Applications. Chapter 9, Cimiano Ontology Learning Textbook Presented by Aaron Stewart. Typical Applications of Ontologies. Agent communication Data integration Description of service capabilities for matching and composition purposes Formal verification of process descriptions

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Luminosity Measurement at the International Linear Collider

Luminosity Measurement at the International Linear Collider. Iftach Sadeh Tel Aviv University ( On behalf of the FCAL collaboration ). December 28 th 2008. http://alzt.tau.ac.il/~sadeh/ sadeh@alzt.tau.ac.il. Overview. 2. The International Linear Collider (ILC).

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A robust adaptive clustering analysis method for automatic identification of clusters

Presenter : Bo- Sheng Wang Authors : P.Y. Mok *, H.Q. Huang, Y.L. Kwok, J.S. Au PR, 2012. A robust adaptive clustering analysis method for automatic identification of clusters. Outlines. Motivation Objectives Methodology Experiments Compary Conclusions Comments. Motivation.

450 views • 29 slides

A Sparsification Approach for Temporal Graphical Model Decomposition

A Sparsification Approach for Temporal Graphical Model Decomposition. Ning Ruan Kent State University. Joint work with Ruoming Jin (KSU), Victor Lee (KSU) and Kun Huang (OSU). Motivation: Financial Markets. Fluorescence Counts. Protein-Protein Interaction.

353 views • 26 slides

The Luminosity Calorimeter

The Luminosity Calorimeter. Iftach Sadeh Tel Aviv University Desy ( On behalf of the FCAL collaboration ). June 11 th 2008. Overview. 2. Intrinsic properties of LumiCal: Luminosity measurement. Design of LumiCal. Performance of LumiCal. Event reconstruction: Bhabha scattering.

325 views • 25 slides

Inclusion of Wave Model Data in Sea State Bias Correction Refinement

Inclusion of Wave Model Data in Sea State Bias Correction Refinement D. Vandemark, N. Tran, H. Feng, B. Chapron, B. Beckley, T. Moore Ocean Surface Topography Science Team, Hobart March 2007. SGT, Inc. APPROACH.

347 views • 24 slides

Clustering Ensembles Using Ant Algorithms

Clustering Ensembles Using Ant Algorithms. Javad Azimi , Paul Cull, Xiaoli Fern { azimi,pc,xfern }@ eecs.oregonstate.edu Oregon State University Presenting by: Paul Cull. Outline. Clustering Ensembles Ant Clustering Proposed Method Experimental Results. Clustering.

543 views • 22 slides

A novel clustering algorithm based on weighted support and its application

A novel clustering algorithm based on weighted support and its application. Author : Xiang-Rong Yang Jun-Yi Shen Qlang Liu Graduate : Chien-Ming Hsiao. Outline. Motivation Objective Introduction Description of some Terms Algorithm and Analysis

281 views • 20 slides

Report: High-Throughput Mapping of a Dynamic Signaling Network in Mammalian Cells

Report: High-Throughput Mapping of a Dynamic Signaling Network in Mammalian Cells. Miriam Barrio-Rodiles and Kevin R. Brown et. al. Science Vol 307 Mar 11 2005 Present by Alex Lei 10/3/2007. Introduction. Why Dynamic protein-protein interaction network (PPI)? Understand the protein functions

175 views • 17 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,

317 views • 16 slides

234 views • 15 slides

Clustering Personalized Web Search Results

270 views • 15 slides

Catching the Trend- A Framework for Clustering Concept-Drifting Categorical Data

Catching the Trend- A Framework for Clustering Concept-Drifting Categorical Data. Hung- Leng Chen, Ming- Syan Chen, and Su-Chen Lin TKDE, Vol.21, No. 5, 2009, pp. 652-665. Presenter : Wei- Shen Tai 200 9 / 7/1. Outline. Introduction Preliminaries Node Importance Representative

333 views • 12 slides

A general grid-clustering approach

A general grid-clustering approach. Presenter : Chun-Ping Wu Authors : Shinong Yue , Miaomiao Wei, Jeen-shing Wang, Huaxiang Wang . ???????? National Yunlin University of Science and Technology. PRL 2008. Outline. Motivation Objective Methodology Experiments Conclusion

213 views • 9 slides

Seabed type clustering

20. Region of Interest. 0. Peak Amplitude. Water column. Seabed substrate. -20. Echo return (dB). -40. Above seabed (m). Below seabed (m). -60. 9.2 m. 5 m. 2.2 m. -80. A. B. 1.3 m. 2.1 m. -100. 0. 50. 100. 150. 200. 250. Distance from sea level (m).

173 views • 1 slides