Clusters and Clustering

Oct 15, 2012

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Clusters and Clustering

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Segmentation and Clustering

Segmentation and Clustering. Today’s Readings Forsyth & Ponce, Chapter 7 (plus lots of optional references in the slides). From Sandlot Science. From images to objects. What Defines an Object? Subjective problem, but has been well-studied Gestalt Laws seek to formalize this

615 views • 49 slides

Similarity and clustering

Similarity and clustering. Motivation. Problem: Query word could be ambiguous: Eg: Query “Star” retrieves documents about astronomy, plants, animals etc. Solution: Visualisation Clustering document responses to queries along lines of different topics.

870 views • 66 slides

Canopy Clustering and K-Means Clustering

Canopy Clustering and K-Means Clustering. Machine Learning Big Data at Hacker Dojo Anandha L Ranganathan (Anand) analog76@gmail.com. Movie Dataset . Download the movie dataset from http :// www.grouplens.org/node/73 The data is in the format UserID :: MovieID ::Rating:: Timestamp

851 views • 35 slides

Capabilities and Clusters

Capabilities and Clusters. Topics 3 & 4: Some New Developments in International Trade National Graduate Institute For Policy Studies IDPTP Fall 2012 John Page. Why Are We Interested in Capabilities?. Recall t he “new” stylized facts about firms:

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Clusters and Superclusters

Clusters and Superclusters. Clusters. Galaxies are not distributed randomly through space Concentrated in groups called Clusters Clusters range from about 10 13 M Sun to about 10 15 M Sun Range in size from 2 Mpc to 10 Mpc across

629 views • 19 slides

Clustering and Modularity

Clustering and Modularity. Global View of Clustering. Clustering is a data mining technique for analyzing structure in a data set. Many many many different criteria available. k-center k-median k-means Inter-Intra etc. k-Center. Minimize maximum distance. k-median.

546 views • 39 slides

Connecticut Career Clusters and Industry Clusters Crosswalk

CT Career Clusters. CT Industry Clusters. Connecticut Career Clusters and Industry Clusters Crosswalk. Agriculture, Food and Natural Resources. Aerospace Agriculture Bioscience Hydrogen-Fuel Cell. Business, Management and Administration. Finance Hospitality and Tourism

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Clustering: Partition Clustering

Clustering: Partition Clustering. Lecture outline. Distance/Similarity between data objects Data objects as geometric data points Clustering problems and algorithms K-means K-median K-center. Inter-cluster distances are maximized. Intra-cluster distances are minimized.

573 views • 34 slides

Clustering and MDS

Clustering and MDS. Exploratory Data Analysis. Outline. What may be hoped for by clustering Representing differences as distances Choosing a clustering method Hierarchical clustering: choosing linkage Multi-dimensional scaling. Legitimate hopes for clustering.

376 views • 15 slides

Clusters of galaxies The ICM, mass measurements and statistical measures of clustering

Clusters of galaxies The ICM, mass measurements and statistical measures of clustering. Plan of this class. The intracluster medium, its origin, dynamics and general properties Evidence of Dark Matter in clusters Masses derived by the virial theorem, x-rays and gravitational lensing

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Clustering and NLP

Clustering and NLP. Slides by me, Sidhartha Shakya, Pedro Domingos, D. Gunopulos, A.L. Yuille, Andrew Moore, and others. Outline. Clustering Overview Sample Clustering Techniques for NLP K-means Agglomerative Model-based (EM). Clustering Overview. What is clustering?.

1.44k views • 76 slides

Hits and Clusters

Hits and Clusters. Rob Kutschke, Caroline Milstene, Hans Wenzel Fermilab May 9, 2007. Overview. Hits first, then clusters. A container of containers: The event contains a HitStore For each sensor the HitStore contains A HitsOnSensor object (plus HitsonSensor)

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Clustering and Network

1. Clustering and Network. Park, Jong Hwa MRC-DUNN Hills Road Cambridge CB2 2XY England. Bioinformatics in Biosophy. Next :. 02/06/2001. Clustering of data is a method by which large sets of data is grouped into clusters of smaller sets of similar data. What is clustering?.

1.2k views • 94 slides

Different Perspectives at Clustering: The “Number-of- Clusters ” Case

Different Perspectives at Clustering: The “Number-of- Clusters ” Case. B. Mirkin School of Computer Science Birkbeck College, University of London IFCS 2006. Different Perspectives at Number of Clusters: Talk Outline. Clustering and K-Means: A discussion

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CLUSTERS and ARRAYS

CLUSTERS and ARRAYS. Array Functions. Build an array Size an array Form an array from a cluster or a cluster into an array Index an array Find the max an min of an array Insert and delete from an array or reshape Sort a lD array Convert an array to matrix, or a matrix to array.

404 views • 13 slides

Clustering and segmentation

Clustering and segmentation. MIS2502 Data Analytics Adapted from Tan, Steinbach, and Kumar (2004). Introduction to Data Mining . http://www-users.cs.umn.edu/~kumar/dmbook/. What is Cluster Analysis?. Grouping data so that elements in a group will be Similar (or related) to one another

495 views • 29 slides

Groups, Clusters and Clusters of Clusters

Groups, Clusters and Clusters of Clusters. Hickson-Arp. Rich Clusters -- nearby Virgo and Fornax. Virgo. Fornax. Virgo. Fornax – X ray. Giant Clusters – Coma and Perseus. Coma. Perseus. Gravitational Lensing. Einstein ring .

305 views • 10 slides

Segmentation and Clustering

Segmentation and Clustering. EECS 598-08 Fall 2014 Foundations of Computer Vision. Instructor: Jason Corso ( jjcorso ) web.eecs.umich.edu /~ jjcorso /t/598F14. Readings: FP 6.2, 9; SZ 5.2-5.4 Date: 10/1 / 14.

1.51k views • 127 slides

Clustering of color sources Fragmentation of clusters Results Conclusions

Multiplicity Distributions and Percolation of Strings J. Dias de Deus and C. Pajares CENTRA,Instituto Superior Tecnico,Lisboa IGFAE,Universidade de Santiago de Compostela. Clustering of color sources Fragmentation of clusters Results Conclusions. CERN, May 14th June 8th 2007.

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Clustering and randomness

Clustering and randomness. Is this random?. Which side is randomly generated?. People Perceive Patterns easily in randomly generated data. Clustering. Event density that occurs on different spatial scales. Is there a characteristic scale?. K-Means clustering (data mining). Code Example.

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CLUSTERING AND AVAILABILITY

Chapter 11. CLUSTERING AND AVAILABILITY. OVERVIEW. Describe the clustering capabilities of Microsoft Windows Server 2003. Understand how to create clusters with Windows Server 2003. Plan a high-availability solution that uses clustering services.

591 views • 41 slides

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