Follow along with the course eBook: http://bit.ly/2JymqYp See the full course: https://systemsacademy.io/courses/network-theory/ Twitter: http://bit.ly/2HobMld The way in which a network is connected plays a large part in how we will analyze and interpret it. When analyzing connectedness and clustering we are asking how integrated or fractured the overall network system is, how these different major sub-systems are distributed out and their local characteristics. Transcription excerpt: The way in which a network is connected plays a large part in how we will analyze and interpret it. When analyzing connectedness and clustering we are asking how integrated or fractured the overall network system is, how these different major sub-systems are distributed out and their local characteristics. A graph can said to be connected if for any node in the graph there is a path to any other node, when the graph is not connected then there will be a number of what we call components to it. A component is a sub-set of nodes and edges within a graph that are fully connected, thus for a node to be part of a component it must be connected to all the other nodes in that component. A cluster is simply a subset of the nodes and edges in a graph that possess certain common characteristics, or relate to each other in a particular ways forming some domain-specific structure. So where as a component is simply referring to whether a given set of nodes are all connected or not, a cluster is referring to how they are connected and how much they are connected that is the frequency of links between a given subset of nodes. In order to model the degree of clustering of a subset of nodes we simply take a node and look at how connect a node it links to is to other nodes that it is also connected to. So if this was a social network of friends we would be asking how many of your friends know your other friends, the more your friends are interconnect the more clustered the subset is said to be. This clustering within social networks is also called a clique, a clique is a group of people who interact with each other more regularly and intensely than others in the same setting. Within this social context clustering can be correlated to homophily, where homophily describes the phenomenon where people tend to form connections with those similar to themselves, as captured in the famous saying “birds of a feather flock together”. We might think of clustering coming from the fact that the interaction between nodes with similar attributes will often require less resources than interaction between nodes with different attributes, for example between to cultures there may be a language barrier or between different devices on a network that might have different protocols, or clustering may be due to physical constraints of the resource expenditure required to maintain them over a greater distance, thus resulting in a clustering around a geographic neighborhood. Understanding the different local conditions that have created clustering within a network are important for understanding why the network is distributed out into the topology that it has, how you can work to integrate it or disintegrate it and how something will propagate across the network, as each one of these clusters will have its own unique set of properties within the whole making it particularly receptive or resistant to a given phenomena. For example we might be analyzing a political network, with each cluster in this network representing a different set of ideologies, social values and policy agendas that are receptive to different messages. Or as another example by understanding that different clustering groups on a computer network may represent different operating systems we will be able to better understand why a virus has rapidly spread in one part of the network but not in another and also by understanding these local clustering condition we will be able to better approach integrating them into the broader network. The clustering coefficient of a node is then a method for measuring the degree of a local cluster, there are a number of such methods for measuring this but they are basically trying to capture the ratio of existing links connecting a nodes neighbors to each other relative to the maximum possible number of such links that could exist between them. A high clustering coefficient for a network is another indication of this small-world phenomena that we saw previously. Twitter: http://bit.ly/2TTjlDH Facebook: http://bit.ly/2TXgrOo LinkedIn: http://bit.ly/2TPqogN
Views: 16820 Systems Academy
Triads, clustering coefficient and neighborhood overlap
Views: 4917 Social Networks
DOWNLOAD Lab Code & Cheat Sheet: https://drive.google.com/open?id=0B2JdxuzlHg7OYnVXS2xNRWZRODQ What is clustering or degree distribution, and how do they affect our interpretation of what’s going on in a network? We define these terms in this video. This video is part of a series where we give you the basic concepts and options, and we walk you through a Lab where you can experiment with designing a network on your own in R. Hosted by Jonathan Morgan and the Duke University Network Analysis Center. Further training materials available at https://dnac.ssri.duke.edu/intro-tutorials.php Duke Network Analysis Center: https://dnac.ssir.duke.edu
Views: 2658 Mod•U: Powerful Concepts in Social Science
An introduction to social network analysis and network structure measures, like density and centrality. Table of Contents: 00:00 - Network Structure 00:12 - Degree Distribution 02:42 - Degree Distribution 06:17 - Density 10:31 - Clustering Coefficient 11:24 - Which Node is Most Important? 12:10 - Which Node is Most Important? 13:27 - Closeness Centrality 15:01 - Closeness Centrality 16:17 - Closeness Centrality 16:36 - Degree Centrality 17:33 - Betweenness Centrality 17:53 - Betweenness Centrality 20:55 - Eigenvector Centrality 23:02 - Connectivity and Cohesion 24:24 - Small Worlds 26:28 - Random Graphs and Small Worlds
Views: 65865 jengolbeck
Tselil Schramm (Simons Institute, UC Berkeley) One of the greatest advantages of representing data with graphs is access to generic algorithms for analytic tasks, such as clustering. In this talk I will describe some popular graph clustering algorithms, and explain why they are well-motivated from a theoretical perspective. ------------------- References from the Whiteboard: Ng, Andrew Y., Michael I. Jordan, and Yair Weiss. "On spectral clustering: Analysis and an algorithm." Advances in neural information processing systems. 2002. Lee, James R., Shayan Oveis Gharan, and Luca Trevisan. "Multiway spectral partitioning and higher-order cheeger inequalities." Journal of the ACM (JACM) 61.6 (2014): 37. ------------------- Additional Resources: In my explanation of the spectral embedding I roughly follow the exposition from the lectures of Dan Spielman (http://www.cs.yale.edu/homes/spielman/561/), focusing on the content in lecture 2. Lecture 1 also contains some additional striking examples of graphs and their spectral embeddings. I also make some imprecise statements about the relationship between the spectral embedding and the minimum-energy configurations of a mass-spring system. The connection is discussed more precisely here (https://www.simonsfoundation.org/2012/04/24/network-solutions/). License: CC BY-NC-SA 4.0 - https://creativecommons.org/licenses/by-nc-sa/4.0/
Views: 8160 GraphXD: Graphs Across Domains
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: 146815 LearnITGuide Tutorials
Dragan Gasevic discusses network modularity and community identification for week 3 of DALMOOC.
Views: 16192 Data Analytics and Learning MOOC
Learn about clustering in networks
Views: 734 Social and Economic Networks
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: 547130 Victor Lavrenko
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/ ...or just donating to StatQuest! https://www.paypal.me/statquest
Views: 86368 StatQuest with Josh Starmer
In this video, I will be introducing my multipart series on clustering algorithms. I introduce clustering, and cover various types of clusterings. Check back soon for part 2. Credit for much of the information used to make this video must go to "Introduction to Data Mining" by Pang-Ning Tan, Michael Steinbach and Vipin Kumar. I refer to the first edition, published in 2006.
Views: 8553 Laurel Powell
. 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. .
Views: 13090 Artificial Intelligence - All in One
#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: 448853 Last moment tuitions
Dr. Steven Skiena, Stony Brook University Michael Hunger, Neo4j Random walk algorithms help better model real-world scenarios, and when applied to graphs, can significantly improve machine learning. Learn how the Deepwalk supervised learning algorithm transfers deep learning techniques from natural language processing to network analysis, and explore the motivations behind graph-enhanced machine learning. #MachineLearning #DeepWalk #NLP
Views: 3099 Neo4j
Hierarchical Clustering - Fun and Easy Machine Learning with Examples ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS Course - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Hierarchical Clustering Looking at the formal definition of Hierarchical clustering, as the name suggests is an algorithm that builds hierarchy of clusters. This algorithm starts with all the data points assigned to a cluster of their own. Then two nearest clusters are merged into the same cluster. In the end, this algorithm terminates when there is only a single cluster left. The results of hierarchical clustering can be shown using Dendogram as we seen before which can be thought of as binary tree Difference between K Means and Hierarchical clustering Hierarchical clustering can’t handle big data well but K Means clustering can. This is because the time complexity of K Means is linear i.e. O(n) while that of hierarchical clustering is quadratic i.e. O(n2). In K Means clustering, since we start with random choice of clusters, the results produced by running the algorithm multiple times might differ. While results are reproducible in Hierarchical clustering. K Means is found to work well when the shape of the clusters is hyper spherical (like circle in 2D, sphere in 3D). K Means clustering requires prior knowledge of K i.e. no. of clusters you want to divide your data into. However with HCA , you can stop at whatever number of clusters you find appropriate in hierarchical clustering by interpreting the Dendogram. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 35825 Augmented Startups
Let's detect the intruder trying to break into our security system using a very popular ML technique called K-Means Clustering! This is an example of learning from data that has no labels (unsupervised) and we'll use some concepts that we've already learned about like computing the Euclidean distance and a loss function to do this. Code for this video: https://github.com/llSourcell/k_means_clustering Please Subscribe! And like. And comment. That's what keeps me going. More learning resources: http://www.kdnuggets.com/2016/12/datascience-introduction-k-means-clustering-tutorial.html http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_ml/py_kmeans/py_kmeans_understanding/py_kmeans_understanding.html http://people.revoledu.com/kardi/tutorial/kMean/ https://home.deib.polimi.it/matteucc/Clustering/tutorial_html/kmeans.html http://mnemstudio.org/clustering-k-means-example-1.htm https://www.dezyre.com/data-science-in-r-programming-tutorial/k-means-clustering-techniques-tutorial http://scikit-learn.org/stable/tutorial/statistical_inference/unsupervised_learning.html Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 107892 Siraj Raval
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: 112119 Bharatendra Rai
Authors: Jingchao Ni, Hanghang Tong, Wei Fan, Xiang Zhang Abstract: Integrating multiple graphs (or networks) has been shown to be a promising approach to improve the graph clustering accuracy. Various multi-view and multi-domain graph clustering methods have recently been developed to integrate multiple networks. In these methods, a network is treated as a view or domain.The key assumption is that there is a common clustering structure shared across all views (domains), and different views (domains) provide compatible and complementary information on this underlying clustering structure. However, in many emerging real-life applications, different networks have different data distributions, where the assumption that all networks share a single common clustering structure does not hold. In this paper, we propose a flexible and robust framework that allows multiple underlying clustering structures across different networks. Our method models the domain similarity as a network, which can be utilized to regularize the clustering structures in different networks. We refer to such a data model as a network of networks (NoN). We develop NoNClus, a novel method based on non-negative matrix factorization (NMF), to cluster an NoN. We provide rigorous theoretical analysis of NoNClus in terms of its correctness, convergence and complexity. Extensive experimental results on synthetic and real-life datasets show the effectiveness of our method. ACM DL: http://dl.acm.org/citation.cfm?id=2783262 DOI: http://dx.doi.org/10.1145/2783258.2783262
Views: 207 Association for Computing Machinery (ACM)
Social network analysis with several simple examples in R. R file: https://goo.gl/CKUuNt Data file: https://goo.gl/Ygt1rg Includes, - Social network examples - Network measures - Read data file - Create network - Histogram of node degree - Network diagram - Highlighting degrees & different layouts - Hub and authorities - Community detection 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: 23655 Bharatendra Rai
Erdos-Reni random graph model. Poisson and Bernulli distributions. Distribution of node degrees. Phase transition, gigantic connected component. Diameter and cluster coefficient. Configuration model Lecture slides: http://www.leonidzhukov.net/hse/2015/networks/lectures/lecture3.pdf
Views: 11590 Leonid Zhukov
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: 40477 DataCamp
MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment and fourth-generation programming language developed by MathWorks. Although MATLAB is intended primarily for numerical computing, but by optional toolboxes, using the MuPAD symbolic engine, has access to symbolic computing capabilities too. One of these toolboxes is Neural Network toolbox. This toolbox is free, open source software for simulating models of brain and central nervous system, based on MATLAB computational platform. In these courses you will learn the general principles of Neural Network Toolbox designed in Matlab and you will be able to use this Toolbox efficiently as well. The list of contents is: Introduction – in this chapter the Neural Network Toolbox is Defined and introduced. An overview of neural network application is provided and the neural network training process for pattern recognition, function fitting and clustering data in demonstrated. Neuron models – A description of the neuron model is provided, including simple neurons, transfer functions, and vector inputs and single and multiple layers neurons are explained. The format of input data structures is very effective in the simulation results of both static and dynamic networks. So this effect is discussed in this chapter too. And finally the incremental and batch training rule is explained. Perceptron networks – In this chapter the perceptron architecture is shown and it is explained how to create a perceptron in Neural network toolbox. The perceptron learning rule and its training algorithm is discussed and finally the network/Data manager GUI is explained. Linear filters – in this chapter linear networks and linear system design function is discussed. The tapped delay lines and linear filters are discussed and at the end of the chapter LMS algorithm and linear classification algorithm used for linear filters are explained. Backpropagation networks – The architecture, simulation, and several high-performance backpropagation training algorithms of backpropagation networks are discussed in this chapter. Conclusion – in this chapter the memory and speed of different backpropagation training algorithms are illustrated. And at the end of the chapter all these algorithms are compared to help you select the best training algorithm for your problem in hand. Matlab Software Installation: You are required to install the Matlab Software on your machine, so you can start executing the codes, and examples we work during the course.
Views: 90 GeoEngineerings School
Graph density. Graph pertitioning. Min cut, ratio cut, normalized and quotient cuts metrics. Spectral graph partitioning (normalized cut). Direct (spectral) modularity maximization. Multilevel recursive partitioning Lecture slides: http://www.leonidzhukov.net/hse/2015/networks/lectures/lecture9.pdf
Views: 9706 Leonid Zhukov
From 25 June to 14 September 2018, 20 interns worked across nine projects. This series is a compilation of their final presentation. Speaker(s): Aldo Glielmo, King's College London Peter Davies, University of Warwick We are currently involved in an exciting re-structuring of our internship programme. More information will be available in January 2019. In the interim, if you are interested in applying for a future internship, please contact the team via [email protected] About the Turing The Alan Turing Institute, headquartered in the British Library, London, was created as the national institute for data science in 2015. In 2017, as a result of a government recommendation, we added artificial intelligence to our remit. The Institute is named in honour of Alan Turing (23 June 1912 – 7 June 1954), whose pioneering work in theoretical and applied mathematics, engineering and computing are considered to be the key disciplines comprising the fields of data science and artificial intelligence. To learn more about what we do, watch our new video on 'What is The Alan Turing Institute' https://www.youtube.com/watch?v=IjS2sVPR2Zc
Views: 141 The Alan Turing Institute
SSAS - Data Mining - Decision Trees, Clustering, Neural networks
Views: 1468 M R Dhandhukia
DOWNLOAD Lab Code & Cheat Sheet: https://drive.google.com/open?id=0B2JdxuzlHg7OYnVXS2xNRWZRODQ Let's go back to our coding example and get started on the positional features of the Discussion and Colleague Networks by looking at transitivity, in-degree, and out-degree measures. This video is part of a series where we give you the basic concepts and options, and we walk you through a Lab where you can experiment with designing a network on your own in R. Hosted by Jonathan Morgan and the Duke University Network Analysis Center. Further training materials available at https://dnac.ssri.duke.edu/intro-tutorials.php Duke Network Analysis Center: https://dnac.ssir.duke.edu
Views: 789 Mod•U: Powerful Concepts in Social Science
In this tutorial, we shift gears and introduce the concept of clustering. Clustering is form of unsupervised machine learning, where the machine automatically determines the grouping for data. There are two major forms of clustering: Flat and Hierarchical. Flat clustering allows the scientist to tell the machine how many clusters to come up with, where hierarchical clustering allows the machine to determine the groupings. https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Views: 59463 sentdex
Quick video to show how you can use Gephi (a free graph visualisation tool) to quickly visualise communities in a network and use some of the features in Gephi like modularity, node ranking, layout (Force Atlas 2) and filtering to analyse the network to detect communities, influencers and information brokers
Views: 9241 Melvin L
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Cluster iris flowers based on petal and sepal size. For more videos, visit http://www.mathworks.com/products/neural-network/examples.html
Views: 18689 MATLAB
Team members : Shruti Hegde, Himanshi Manglunia, Kalpita Raut, Vyjayanthi Kamath Project: Airbnb Data Analysis
Views: 34 Kalpita Raut
Big Data Fundamentals is part of the Big Data MicroMasters program offered by The University of Adelaide and edX. Learn how big data is driving organisational change and essential analytical tools and techniques including data mining and PageRank algorithms. Enrol now! http://bit.ly/2rg1TuF
Views: 1143 BigDataX: Big Data Fundamentals
Energy Efficient Clustering Algorithm for Multi-Hop Wireless Sensor Network Using Type-2 Fuzzy Logic To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org Lifetime enhancement has always been a crucial issue as most of the wireless sensor networks (WSNs) operate in unattended environment where human access and monitoring are practically infeasible. Clustering is one of the most powerful techniques that can arrange the system operation in associated manner to attend the network scalability, minimize energy consumption, and achieve prolonged network lifetime. To conquer this issue, current researchers have triggered the proposition of many numerous clustering algorithms. However, most of the proposed algorithms overburden the cluster head (CH) during cluster formation. To overcome this problem, many researchers have come up with the idea of fuzzy logic (FL), which is applied in WSN for decision making. These algorithms focus on the efficiency of CH, which could be adoptive, flexible, and intelligent enough to distribute the load among the sensor nodes that can enhance the network lifetime. But unfortunately, most of the algorithms use type-1 FL (T1FL) model. In this paper, we propose a clustering algorithm on the basis of interval type-2 FL model, expecting to handle uncertain level decision better than T1FL model.
Views: 1560 jpinfotechprojects
In recent years, one of the most popular modern clustering algorithms in various fields like Network Science applications, Data mining, Pattern Recognition, etc., are spectral clustering algorithms. Another area of research interest found much attractive recently are Wireless Sensor Networks(WSNs), consisting of low power, low-cost, and energy-constrained sensors employed to monitor and report a physical phenomenon to the sink node where the end-user can access the data. Some of the important challenges in WSNs are increasing network longevity and decreasing consumption of sensor energy. To handle these clustering algorithms can be utilized and use spectral graph theory in order to subdivide the network such that each cluster includes the highest inter-correlated sensors. In this presentation, we widely analyze the need and efficacy of use of Spectral Clustering algorithms from a network Graph partitioning point of view. To do this, some vital aspects of spectral clustering was studied; Also analysis and actual implementation of meaningful partition of simple data sets was employed. This was followed by deriving and subsequent implementations of two typical spectral clustering algorithms, namely, ratio-cuts and normalized-cuts thus providing sufficient insight by proposing experiments on large web-graphs and thereafter discussing/analyzing the results. Finally, using a neoteric approach called K-Way Spectral Clustering Algorithm in Wireless Sensor Network (KSCA-WSN) we can try to address its aforementioned challenges. Experimental results and subsequent simulations and observations attest to the fact that this implementation produces performance of better quality, they give a good approximation of the min-cut graph partitioning problem in terms of reducing the cut size and KSCA-WSNs help in effectively distributing the overall consumption of sensor energy as well as ensuring larger network lifetimes, taking into account quantitative as well as visual evaluations.
Views: 174 Sankalp Mohanty
Provides steps for carrying out time-series analysis with R and covers clustering stage. Previous video - time-series forecasting: https://goo.gl/wmQG36 Next video - time-series classification: https://goo.gl/w3b55p Time-Series videos: https://goo.gl/FLztxt Machine Learning videos: https://goo.gl/WHHqWP Becoming Data Scientist: https://goo.gl/JWyyQc Introductory R Videos: https://goo.gl/NZ55SJ Deep Learning with TensorFlow: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi Text mining: https://goo.gl/7FJGmd Data Visualization: https://goo.gl/Q7Q2A8 Playlist: https://goo.gl/iwbhnE 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: 1395 Bharatendra Rai
This is a demonstration of a self organizing map or Kohonen Network being used for image clustering. Image data of 16x16 web icons are provided and statistical analysis of the image data is performed to extra mean and standard deviations of RGB components as a whole and in subsections of the images. The total components (26 dimensions) are then reorganized using the SOM as follows. Source code: https://onedrive.live.com/redir?resid=78920BE92C3F390F!234657&authkey=!APnIvFmxgJ_gWQU&ithint=file%2czip
Views: 24915 Adam Stirtan
This video is going to talk about K-means algorithm for clustering analysis in Tableau. It will also generate a model to identify which variable contribute the most to separate the clusters, as well as the center for each cluster. Thanks for watching. My website: http://allenkei.weebly.com If you like this video please "Like", "Subscribe", and "Share" it with your friends to show your support! If there is something you'd like to see or you have question about it, feel free to let me know in the comment section. I will respond and make a new video shortly for you. Your comments are greatly appreciated. Advertisement: World Class Assignment/Homework Help for Students. Check out the website links given below: GeniAssist Info. : https://geniassist17.wixsite.com/website Online Bio-Tutors (For Life Science/Health Science students only): https://akashbararia12.wixsite.com/onlinebiotutors
Views: 3532 Allen Kei