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Views: 2462 Ryo Eng

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Definition,Measures , Application & Examples Cluster Analysis
Views: 254 Dr.Anamika Bhargava

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This is short tutorial for What it is? (What do we mean by a cluster?) How it is different from decision tree? What is distance and linkage function? What is hierarchical clustering? What is scree plot & dendogram? What is non hierarchical clustering (k-means)? How to learn it in detail (step by step)? --------------------------------- Read in great detail along with Excel output, computation and SAS code ---------------------------------- https://www.udemy.com/cluster-analysis-motivation-theory-practical-application/?couponCode=FB_CA_001
Views: 136900 Gopal Malakar

08:47
. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .

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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag discusses clustering. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 90099 MIT OpenCourseWare

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What is clustering Partitioning a data into subclasses. Grouping similar objects. Partitioning the data based on similarity. Eg:Library. Clustering Types Partitioning Method Hierarchical Method Agglomerative Method Divisive Method Density Based Method Model based Method Constraint based Method These are clustering Methods or types. Clustering Algorithms,Clustering Applications and Examples are also Explained.

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Provides illustration of doing cluster analysis with R. R File: https://goo.gl/BTZ9j7 Machine Learning videos: https://goo.gl/WHHqWP Includes, - Illustrates the process using utilities data - data normalization - hierarchical clustering using dendrogram - use of complete and average linkage - calculation of euclidean distance - silhouette plot - scree plot - nonhierarchical k-means clustering Cluster analysis is an important tool related to analyzing big data or working in data science field. Deep Learning: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 108559 Bharatendra Rai

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Título: Cluster analysis: application to molecular variability studies Descripción: The objective of this Polimedia is to provide the student with the theorical basis of cluster analysis applied to variability studies with molecular markers. A concrete example is developed based on dominant molecular markers. Autor/a: Pérez De Castro Ana María + Universitat Politècnica de València UPV: https://www.upv.es + Más vídeos en: https://www.youtube.com/valenciaupv + Accede a nuestros MOOC: https://upvx.es

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Views: 32776 Great Learning

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Views: 23617 Educate Motivate

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Clustering is the process of grouping the data into classes or clusters so that objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters.
Views: 10926 Red Apple Tutorials

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The basic scenario is as follows: To extract a region coordinates from a 2D grid. A file in Arc Grid format first has header (attribute) information about the 2D grid and then followed by a grid itself. The value in each cell is the intensity of the area represented by that cell. If this value is zero then the area represented by that cell represent an empty area. Each connected set of cells with same intensity represents a region of that intensity. A region can have holes, this means that in an interior of a region there can be a cells of other intensity or intensity value zero. So, problem is extract each such region with a set of hole cycles. Many approaches are available for the study of the data; these include representation of data in most defined form, reduction in noise, etc. While the various methods have been developed for the above mentioned purpose there still exist some complications. And sometimes these methods cannot be applied on all kind of data set; data set with varying noise, dimensions, variables. The focus of my project is to implement cluster algorithm and to validate the obtained result. This is particularly important for the protection of bad cluster formation and reduction of noise (irrelevant data objects).
Views: 123 K Brat

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Views: 32703 Augmented Startups

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This video is about KMedoid Clustering with NLP example

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Views: 6 Eli Vaughn

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K-means clustering is used in all kinds of situations and it's crazy simple. Example R code in on the StatQuest website: https://statquest.org/2017/07/05/statquest-k-means-clustering/ For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt... https://teespring.com/stores/statquest ...or buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/

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Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://goo.gl/to1yMH or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 if you have any query email us at [email protected] or [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 26756 Last moment tuitions

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Views: 8541 Great Learning

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Views: 109 Cristian BEZA

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Views: 1 el-mehdi chouki

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How to generate a Scree Plot for Hierarchical Cluster in R? ------------------ for more details on cluster analysis using SAS and R -- https://www.udemy.com/cluster-analysis-motivation-theory-practical-application/?couponCode=FB_CA_001 -------------------------------------- link to R code - https://drive.google.com/file/d/0Byo-GmbU7XciVGRQcTk3QzdTMjA/view?usp=sharing
Views: 2066 Gopal Malakar

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Views: 102554 Siraj Raval

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Module XXII - CLUSTERING CLUSTERING: Clustering is an important form of unsupervised learning (i.e., extracting patterns from unlabeled data). These two videos discuss how Kruskal's MST algorithm suggests flexible and useful greedy approaches to clustering problems.
Views: 226 intrigano

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Views: 1 el-mehdi chouki

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#kmean datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 389022 Last moment tuitions

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A step by step guide of how to run k-means clustering in Excel. Please note that more information on cluster analysis and a free Excel template is available at http://www.clusteranalysis4marketing.com
Views: 96824 MktgStudyGuide

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In this video I walk you through how to run and interpret a hierarchical cluster analysis in SPSS and how to infer relationships depicted in a dendrogram. Here is a link to the data: https://drive.google.com/file/d/0B3T1TGdHG9aEbXBEMnZxQU43Qjg/view?usp=sharing

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Views: 69731 edureka!

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Full lecture: http://bit.ly/K-means The K-means algorithm starts by placing K points (centroids) at random locations in space. We then perform the following steps iteratively: (1) for each instance, we assign it to a cluster with the nearest centroid, and (2) we move each centroid to the mean of the instances assigned to it. The algorithm continues until no instances change cluster membership.
Views: 529130 Victor Lavrenko

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Attribute data and relationship data are two principle types of data, representing the intrinsic and extrinsic properties of entities. While attribute data has been the main source of data for cluster analysis, relationship data such as social networks or metabolic networks are becoming increasingly available. In many cases these two data types carry complementary information, which calls for a joint cluster analysis of both data types in order to achieve more natural clusterings. For example, when identifying research communities, relationship data could represent co-author relationships and attribute data could represent the research interests of scientists. Communities could then be identified as clusters of connected scientists with similar research interests. Our introduction of joint cluster analysis is part of a recent, broader trend to consider as much background information as possible in the process of cluster analysis, and in general, in data mining. In this talk, we briefly review related work including constrained clustering, semi-supervised clustering and multi-relational clustering. We then propose the Connected k-Center (CkC) problem, which aims at finding k connected clusters minimizing the radius with respect to the attribute data. We sketch the main ideas of the proof of NP-completeness and present a constant factor approximation algorithm for the CkC problem. Since this algorithm does not scale to large datasets, we have also developed NetScan, a heuristic algorithm that is efficient for large, real databases. We report experimental results from two applications, community identification and document clustering, both based on DBLP data. Our experiments demonstrate that NetScan finds clusters that are more meaningful and accurate than the results of existing algorithms. We conclude the talk with other promising applications and new problems of joint cluster analysis. In particular, we discuss the clustering of gene expression data and the hotspot analysis of crime data as well as a joint cluster analysis problem that does not require the user to specify the number of clusters in advance.
Views: 49 Microsoft Research

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The scikit learn library for python is a powerful machine learning tool. K means clustering, which is easily implemented in python, uses geometric distance to create centroids around which our data can fit as clusters. In the example attached to this article, I view 99 hypothetical patients that are prompted to sync their smart watch healthcare app data with a research team. The data is recorded continuously, but to comply with healthcare regulations, they have to actively synchronize the data. This example works equally well is we consider 99 hypothetical customers responding to a marketing campaign. In order to prompt them, several reminder campaigns are run each year. In total there are 32 campaigns. Each campaign consists only of one of the following reminders: e-mail, short-message-service, online message, telephone call, pamphlet, or a letter. A record is kept of when they sync their data, as a marker of response to the campaign. Our goal is to cluster the patients so that we can learn which campaign type they respond to. This can be used to tailor their reminders for the next year. In the attached video, I show you just how easy this is to accomplish in python. I use the python kernel in a Jupyter notebook. There will also a mention of dimensionality reduction using principal component separation, also done using scikit learn. This is done so that we can view the data as a scatter plot using the plotly library.
Views: 40762 Juan Klopper

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K-means sorts data based on averages. Dr Mike Pound explains how it works. Fire Pong in Detail: https://youtu.be/ZoZMMg1r_Oc Deep Dream: https://youtu.be/BsSmBPmPeYQ FPS & Digital Video: https://youtu.be/yniSnYtkrwQ Dr. Mike's Code: % This script is the one mentioned during the Computerphile Image % Segmentation video. I chose matlab because it's a popular tool for % quickly prototyping things. Matlab licenses are pricey, if you don't have % one (or, like me, work for an organisation that does) try Octave as a % good free alternative. This code should work in Octave too. % Load in an input image im = imread('C:\Path\Of\Input\Image.jpg'); % In matlab, K-means operates on a 2D array, where each sample is one row, % and the features are the columns. We can use the reshape function to turn % the image into this format, where each pixel is one row, and R,G and B % are the columns. We are turning a W,H,3 image into W*H,3 % We also cast to a double array, because K-means requires it in matlab imflat = double(reshape(im, size(im,1) * size(im,2), 3)); % I specify that initialisation shuold sample points at % random, rather than anything complex like kmeans++ initialisation. % Kmeans++ takes a long time if you are using 256 classes. % Perform k-means. This function returns the class IDs assigned to each % pixel, and in this case we also want the mean values for each class - % what colour is each class. This can take a long time if the value for K % is large, I've used the sampling start strategy to speed things up. % While KMeans is running, it will show you the iteration count, and the % number of pixels that have changed class since last iteration. This % number should get lower and lower, as the means settle on appropriate % values. For large K, it's unlikely that we will ever reach zero movement % (convergence) within 150 iterations. K = 3 [kIDs, kC] = kmeans(imflat, K, 'Display', 'iter', 'MaxIter', 150, 'Start', 'sample'); % Matlab can output paletted images, that is, grayscale images where the % colours are stored in a separate array. This array is kC, and kIDs are % the grayscale indices. colormap = kC / 256; % Scale 0-1, since this is what matlab wants % Reshape kIDs back into the original image shape imout = reshape(uint8(kIDs), size(im,1), size(im,2)); % Save file out, you need to subtract 1 from the image classes, since once % stored in the file the values should go from 0-255, not 1-256 like matlab % would do. imwrite(imout - 1, colormap, 'C:\Path\Of\Output\Image.png'); http://www.facebook.com/computerphile https://twitter.com/computer_phile This video was filmed and edited by Sean Riley. Computer Science at the University of Nottingham: http://bit.ly/nottscomputer Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com
Views: 181426 Computerphile

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Views: 27262 Simplilearn

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Description
Views: 39 xind xrci

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Guest Speaker: Sira Sriswasdi website https://cmbcu.github.io/profile_ss.html Slides: https://ekapolc.github.io/slides/pattern/L2-BioinformaticsApp.pdf
Views: 2489 Ekapol Chuangsuwanich

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Views: 13545 Cognitive Class

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Learn the basics of Machine Learning with R. Start our Machine Learning Course for free: https://www.datacamp.com/courses/introduction-to-machine-learning-with-R First up is Classification. A *classification problem* involves predicting whether a given observation belongs to one of two or more categories. The simplest case of classification is called binary classification. It has to decide between two categories, or classes. Remember how I compared machine learning to the estimation of a function? Well, based on earlier observations of how the input maps to the output, classification tries to estimate a classifier that can generate an output for an arbitrary input, the observations. We say that the classifier labels an unseen example with a class. The possible applications of classification are very broad. For example, after a set of clinical examinations that relate vital signals to a disease, you could predict whether a new patient with an unseen set of vital signals suffers that disease and needs further treatment. Another totally different example is classifying a set of animal images into cats, dogs and horses, given that you have trained your model on a bunch of images for which you know what animal they depict. Can you think of a possible classification problem yourself? What's important here is that first off, the output is qualitative, and second, that the classes to which new observations can belong, are known beforehand. In the first example I mentioned, the classes are "sick" and "not sick". In the second examples, the classes are "cat", "dog" and "horse". In chapter 3 we will do a deeper analysis of classification and you'll get to work with some fancy classifiers! Moving on ... A **Regression problem** is a kind of Machine Learning problem that tries to predict a continuous or quantitative value for an input, based on previous information. The input variables, are called the predictors and the output the response. In some sense, regression is pretty similar to classification. You're also trying to estimate a function that maps input to output based on earlier observations, but this time you're trying to estimate an actual value, not just the class of an observation. Do you remember the example from last video, there we had a dataset on a group of people's height and weight. A valid question could be: is there a linear relationship between these two? That is, will a change in height correlate linearly with a change in weight, if so can you describe it and if we know the weight, can you predict the height of a new person given their weight ? These questions can be answered with linear regression! Together, \beta_0 and \beta_1 are known as the model coefficients or parameters. As soon as you know the coefficients beta 0 and beta 1 the function is able to convert any new input to output. This means that solving your machine learning problem is actually finding good values for beta 0 and beta 1. These are estimated based on previous input to output observations. I will not go into details on how to compute these coefficients, the function lm() does this for you in R. Now, I hear you asking: what can regression be useful for apart from some silly weight and height problems? Well, there are many different applications of regression, going from modeling credit scores based on past payements, finding the trend in your youtube subscriptions over time, or even estimating your chances of landing a job at your favorite company based on your college grades. All these problems have two things in common. First off, the response, or the thing you're trying to predict, is always quantitative. Second, you will always need input knowledge of previous input-output observations, in order to build your model. The fourth chapter of this course will be devoted to a more comprehensive overview of regression. Soooo.. Classification: check. Regression: check. Last but not least, there is clustering. In clustering, you're trying to group objects that are similar, while making sure the clusters themselves are dissimilar. You can think of it as classification, but without saying to which classes the observations have to belong or how many classes there are. Take the animal photo's for example. In the case of classification, you had information about the actual animals that were depicted. In the case of clustering, you don't know what animals are depicted, you would simply get a set of pictures. The clustering algorithm then simply groups similar photos in clusters. You could say that clustering is different in the sense that you don't need any knowledge about the labels. Moreover, there is no right or wrong in clustering. Different clusterings can reveal different and useful information about your objects. This makes it quite different from both classification and regression, where there always is a notion of prior expectation or knowledge of the result.
Views: 39662 DataCamp

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SImplest Video about density based algorithm - DBSCAN
Views: 41294 Red Apple Tutorials

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Views: 6879 Ryo Eng

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Learn : Application of analytics in the insurance industry. Agenda will be -Customer lifecycle in insurance business -Fundamental of insurance business -Application of various analytics techniques along with the customer life cycle 1. Use of predictive analytics / supervised machine learning algorithms 2. Use of collaborative filtering etc. Take a look at my other publications --------------------------------------------------------------- Learn R programming https://www.udemy.com/introduction-to-r-programming-learn-r-syntax-by-example/?couponCode=DSC_01_01 Statistics by simulation https://www.udemy.com/statistics-by-example/?couponCode=DSC_01_01 Logistic Regression https://www.udemy.com/logistic-regression-workshop-using-r-step-by-step-modeling/?couponCode=DSC_01_01 PCA and Factor Analysis https://www.udemy.com/principal-component-analysis-pca-and-factor-analysis/?couponCode=DSC_01_01 decision tree - CHAID - CART - Random Forest https://www.udemy.com/decision-tree-theory-application-and-modeling-using-r/?couponCode=FB_DT_01 Cluster analysis https://www.udemy.com/cluster-analysis-motivation-theory-practical-application/?couponCode=YTB Neural Network https://www.udemy.com/artificial-neural-networks-tutorial-theory-applications/?couponCode=GT_01_ANN
Views: 4581 Gopal Malakar

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ACCESS the FULL COURSE here: https://academy.zenva.com/product/data-science-mini-degree/?zva_src=youtube-datascience-md TRANSCRIPT In this video, we are going to learn a little bit about cluster analysis. And this is a topic that we're gonna be discussing over the duration of this course. So just to give you an overview of the different things we're gonna be covering, I'm gonna give you an introduction to cluster analysis, basically what is it and what are the different applications of it, as well as what kind of algorithms can we expect. And in fact, we're gonna be covering three very popular algorithms, k-means clustering, DBSCAN, which stands for density-based spatial clustering of applications with noise, but usually we just call it DBSCAN, and then hierarchical agglomerative clustering, HAC. These are three very popular clustering algorithms. And the interesting thing is they all take very different approaches to creating clusters. And we're gonna get into all those in the subsequent videos. But first let's talk a little bit about cluster analysis. And that's what we're gonna be focusing on primarily in this video, just to acquaint you with some of the terminology as well as some applications of cluster analysis for example. So clustering analysis, so imagine we have some data. The whole point of clustering analysis is in an unsupervised way with no a priori information, we want to be able to separate different groups based on the data that we have. And now sometimes these groups are predefined. You have the set of data like in this case, and you say, well, this seems, we plot this data, and you say, well, it seems to fit into two little groups. Here there's a little clustering of points on the bottom left, and there's a larger, kind of elongated cluster on the top right and so we might say, well, we can give a predefined number of clusters. We want two clusters and we can give that to the clustering algorithms and then they'll group these guys together. It'll make a split and it actually, in some cases, we don't need to specify the number of clusters. In fact, some algorithms, which is DBSCAN, are actually smart enough to be able to figure out how many clusters are based entirely on the data. But algorithms like k-means will actually need to be specified how many clusters that we have. And so, for example, this data scan is actually taken, it's a very famous data set called the Iris Dataset, collected by Ronald Fisher, which is, and here is a quick historical side note, he's probably the most important statistician of the 20th century. A lot of statistical techniques that we have that are used in all kinds of companies were originally some of his work, but he collected this dataset of flowers. He has 50 different of three different kinds of species of flowers and he plots their measured properties like petal width, petal length, sepal width, and sepal length, and they're all plotted out. In this case, what I've actually done is removed the class labels, because usually when we're doing clustering analysis, we don't have the correct labels. In fact, that's what the clustering is trying to give us. It's trying to give us some notion that these things belong together and these other things belong together. So this is just a kind of data that you might expect with some clustering. So clustering is taking our data and then putting it into groups such that the groups have some kind of similar properties or similar attributes here. So if we go back a slide here, so we have one cluster at the bottom left for example. That might be considered a cluster where the flowers in that cluster have a small petal length and a smaller petal width, for example. That's an example of grouping, as I'm talking about. And there's so many different applications of clustering analysis, not just used for something like data science. But also things like medical imaging for things like x-rays or MRIs or FMRIs. They use clustering analysis. Free Tutorials: - Unity: https://gamedevacademy.org - Phaser: https://phasertutorials.com - Machine Learning: https://pythonmachinelearning.pro - Web Dev: https://html5hive.org - Android: https://androidkennel.org - Swift: https://swiftludus.org - VR: https://vrgamedevelopment.pro
Views: 33 Zenva

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Views: 6796 Omar Sobh

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IEEE CIS Cyprus Chapter, Region 8 Date: 31 October 2012 Time: 17:00 -- 19:00 Location: University of Cyprus, Cyprus Presentation: http://www.slideshare.net/ieee_cis_cyprus/prof-jim-bezdek-every-picture-tells-a-story-visual-cluster-analysis The talk overviews the history of Visual Clustering, which began thousands of years ago. The first image for this appeared in 1873. Three algorithms for visual assessment of clustering tendency examined, namely the VAT, iVAT and asiVAT, with applications to social network analysis. Particularly three applications, one for each algorithm will be discussed: time series analysis with clusters of linguistic medoid prototypes in Eldercare data (iVAT); social network analysis with Sampson's Monastery data (asiVAT); and network access security (VAT), a commercial application developed by CA technologies. Help us caption & translate this video! http://amara.org/v/CjLm/
Views: 814 CIS Cyprus

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Sudhir Voleti's how-to video for cluster-analysis Shiny app for my Business and Analytics students.
Views: 1471 Sudhir Voleti

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Whenever we look at a map, it is natural for us to organize, group, differentiate, and cluster what we see to help us make better sense of it. This session will explore the powerful Spatial Statistics techniques designed to do just that: Hot Spot Analysis and Cluster and Outlier Analysis. We will demonstrate how these techniques work and how they can be used to identify significant patterns in our data. We will explore the different questions that each tool can answer, best practices for running the tools, and strategies for interpreting and sharing results. This comprehensive introduction to cluster analysis will prepare you with the knowledge necessary to turn your spatial data into useful information for better decision making.
Views: 28764 Esri Events

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This video explains you about "What is Cluster? Why do we need Cluster? what are the types of Clusters? and Understand the Basic Cluster Concepts for Beginners". COMPLETE OTHER TECHNOLOGY FULL TRAINING AND TUTORIAL VIDEOS PLAYLISTS: Devops Tutorial & Devops Online Training - https://goo.gl/hpQNz3 Puppet Tutorial & Puppet Online Training - https://goo.gl/wbikT9 Ansible Tutorial & Ansible Online Training - https://goo.gl/kQc7HV Docker Tutorial & Docker Online Training - https://goo.gl/x3nXPg Python Programming Tutorial & Python Online Training - https://goo.gl/hDN4Ai Cloud Computing Tutorial & Cloud Computing Online Training - https://goo.gl/Dnez3Q Openstack Tutorial & Openstack Online Training - https://goo.gl/hEK9n9 Clustering Tutorial & Clustering Online Training - https://goo.gl/FvdmMQ VCS Cluster Tutorial & Veritas Cluster Online Training - https://goo.gl/kcEdJ5 Ubuntu Linux Tutorial & Ubuntu Online Training - https://goo.gl/pFrfKK RHCSA and RHCE Tutorial & RHCSA and RHCE Online Training - https://goo.gl/qi2Xjf Linux Tutorial & Linux Online Training - https://goo.gl/RzGUb3 Subscribe our channel "LearnITGuide Tutorials" for more updates and stay connected with us on social networking sites, Youtube Channel : https://goo.gl/6zcLtQ Facebook : http://www.facebook.com/learnitguide Twitter : http://www.twitter.com/learnitguide Visit our Website : https://www.learnitguide.net #cluster #highavailabilty #loadbalancer cluster tutorial, cluster tutorial for beginners, clustering tutorial, server clustering tutorial, linux cluster tutorial, cluster concepts, cluster basics, cluster video, cluster tutorial videos, cluster basic concepts, basic cluster concepts, how cluster works, introduction to cluster, introduction to clustering, clustering tutorials, understand cluster concepts, cluster concepts for beginners, high availability cluster tutorial, server clustering concepts, clustering tutorials
Views: 138027 LearnITGuide Tutorials

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