Search results “Clustering in network analysis”

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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.
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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.

Views: 13825
Complexity Labs

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: 1241
Mod•U: Powerful Concepts in Social Science

Learn about clustering in networks

Views: 523
Social and Economic Networks

Triads, clustering coefficient and neighborhood overlap

Views: 2727
Social Networks

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: 4536
GraphXD: Graphs Across Domains

Cohesive subgroups. Graph cliques, k-plexes, k-cores. Network communities. Vertex similarity matrix. Similarity based clustering. Agglomerative clustering. Graph partitioning. Repeated bisection. Edge Betweenness. Newman-Girvin algorithm.
Lecture slides:
http//www.leonidzhukov.net/hse/2015/networks/lectures/lecture8.pdf

Views: 6016
Leonid Zhukov

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: 66505
MIT OpenCourseWare

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: 58521
jengolbeck

Manufacturing Systems Management by Prof. G. Srinivasan, Department of Management Studies, IIT Madras. For more details on NPTEL visit http://nptel.iitm.ac.in

Views: 20853
nptelhrd

Get a Free Trial: https://goo.gl/C2Y9A5
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Cluster iris flowers based on petal and sepal size. For more videos, visit http://www.mathworks.com/products/neural-network/examples.html

Views: 16968
MATLAB

What's a computer cluster? How does it work? How does it handle work for a whole lot of people at once?

Views: 66554
Ross Dickson

.
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: 5623
Artificial Intelligence - All in One

Dr. Debra Knisley, ETSU Department of Mathematics and Statistics
MATH 5840 - Complex Networks and Systems
ETSU Online Programs - http://www.etsu.edu/online

Views: 112
East Tennessee State University

A quick tutorial on how to use gephi's modularity feature to detect communities and color code them in graphs.

Views: 53696
jengolbeck

Basics of Social Network Analysis
In this video Dr Nigel Williams explores the basics of Social Network Analysis (SNA): Why and how SNA can be used in Events Management Research.
The freeware sound tune 'MFF - Intro - 160bpm' by Kenny Phoenix http://www.last.fm/music/Kenny+Phoenix was downloaded from Flash Kit
http://www.flashkit.com/loops/Techno-Dance/Techno/MFF_-_In-Kenny_Ph-10412/index.php
The video's content includes:
Why Social Network Analysis (SNA)?
Enables us to segment data based on user behavior.
Understand natural groups that have formed:
a. topics
b. personal characteristics
Understand who are the important people in these groups.
Analysing Social Networks:
Data Collection Methods:
a. Surveys
b. Interviews
c. Observations
Analysis:
a. Computational analysis of matrices
Relationships:
A. is connected to B.
SNA Introduction:
[from] A. Directed Graph [to] B. e.g. Twitter replies and mentions
A. Undirected Graph B. e.g. family relationships
What is Social Network Analysis?
Research technique that analyses the Social structure that emerges from the combination of relationships among members of a given population (Hampton & Wellman (1999); Paolillo (2001); Wellman (2001)).
Social Network Analysis Basics: Node and Edge
Node:
“actor” or people on which relationships act
Edge:
relationship connecting nodes; can be directional
Social Network Analysis Basics: Cohesive Sub-group
Cohesive Sub-group:
a. well-connected group, clique, or cluster, e.g. A, B, D, and E
Social Network Analysis Basics: Key Metrics
Centrality (group or individual measure):
a. Number of direct connections that individuals have with others in the group (usually look at incoming connections only).
b. Measure at the individual node or group level.
Cohesion (group measure):
a. Ease with which a network can connect.
b. Aggregate measure of shortest path between each node pair at network level reflects average distance.
Density (group measure):
a. Robustness of the network.
b. Number of connections that exist in the group out of 100% possible.
Betweenness (individual measure):
a. Shortest paths between each node pair that a node is on.
b. Measure at the individual node level.
Social Network Analysis Basics: Node Roles:
Node Roles:
Peripheral – below average centrality, e.g. C.
Central connector – above average centrality, e.g. D.
Broker – above average betweenness, e.g. E.
References and Reading
Hampton, K. N., and Wellman, B. (1999). Netville Online and Offline Observing and Surveying a Wired Suburb. American Behavioral Scientist, 43(3), pp. 475-492.
Smith, M. A. (2014, May). Identifying and shifting social media network patterns with NodeXL. In Collaboration Technologies and Systems (CTS), 2014 International Conference on IEEE, pp. 3-8.
Smith, M., Rainie, L., Shneiderman, B., and Himelboim, I. (2014). Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters. Pew Research Internet Project.

Views: 34358
Alexandra Ott

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: 92055
Bharatendra Rai

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: 8030
Leonid Zhukov

This is a clip from the Clustering module of our course on data analytics by Gaurav Vohra, founder of Jigsaw Academy.
Jigsaw Academy is an award winning premier online analytics training institute that aims to meet the growing demand for talent in the field of analytics by providing industry-relevant training to develop business-ready professionals.Jigsaw Academy has been acknowledged by blue chip companies for quality training
Follow us on:
https://www.facebook.com/jigsawacademy
https://twitter.com/jigsawacademy
http://jigsawacademy.com/

Views: 97053
Jigsaw Academy

We address the problem of acoustic source separation in a deep learning framework we call "deep clustering". Rather than directly estimating signals or masking functions, we train a deep network to produce spectrogram embeddings that are discriminative for partition labels given in training data. Previous deep network approaches provide great advantages in terms of learning power and speed, but previously it has been unclear how to use them to separate signals in a class-independent way. In contrast, spectral clustering approaches are flexible with respect to the classes and number of items to be segmented, but it has been unclear how to leverage the learning power and speed of deep networks. To obtain the best of both worlds, we use an objective function that to train embeddings that yield a low-rank approximation to an ideal pairwise affinity matrix, in a class-independent way. This avoids the high cost of spectral factorization and instead produces compact clusters that are amenable to simple clustering methods. The segmentations are therefore implicitly encoded in the embeddings, and can be "decoded" by clustering. Preliminary experiments show that the proposed method can separate speech: when trained on spectrogram features containing mixtures of two speakers, and tested on mixtures of a held-out set of speakers, it can infer masking functions that improve signal quality by around 6dB. We show that the model can generalize to three-speaker mixtures despite training only on two-speaker mixtures. The framework can be used without class labels, and therefore has the potential to be trained on a diverse set of sound types, and to generalize to novel sources. We hope that future work will lead to segmentation of arbitrary sounds, with extensions to microphone array methods as well as image segmentation and other domains.

Views: 2096
Microsoft Research

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: 6711
Laurel Powell

This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.

Views: 4946
Udacity

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".
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Views: 94572
LearnITGuide Tutorials

Learn more advanced front-end and full-stack development at: https://www.fullstackacademy.com
Gephi is an open-source network analysis software package written in Java that allows us to visualize all kinds of graphs and networks. In this Gephi tutorial, we walk through how Network Analysis can be used to visually represent large data sets in a way that enables the viewer to get a lot of value from the data just by looking briefly at the graph.
Watch this video to learn:
- What Network Analysis involves
- How to use Gephi to visually represent and analyze data sets
- Different examples using Gephi

Views: 12146
Fullstack Academy

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: 9812
Leonid Zhukov

Networks encode complex information on all kinds of interactions. We look at how a network perspective can reveal valuable information about corruption in public procurement, internal collaboration at a multinational firm, and the tone of campaigns on Twitter, all with the phenomenal NetworkX library. NetworkX is a highly productive and actively maintained library that interacts well with other libraries and environments. It inherits many Python strengths like fast prototyping and ease of teaching. We also discuss alternatives like graph-tool and igraph.

Views: 14119
PyCon SK

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
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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
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Views: 79102
Siraj Raval

Illustrative video for the Medium Article 'Visualizing My Network Clusters'. Made with Gephi.

Views: 666
Ashris Choudhury

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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: 9134
Artificial Intelligence - All in One

Random walks on graph. Stationary distribution.
Physical diffusion. Diffusion equation. Diffusion in networks.
Discrete Laplace operator, Laplace matrix. Solution of the diffusion
equation. Normalized Laplacian.
Lecture slides: http://www.leonidzhukov.net/hse/2015/networks/lectures/lecture11.pdf

Views: 5662
Leonid Zhukov

This tutorial goes from import through the whole analysis phase for a citation network. Data can be accessed at http://www.cs.umd.edu/~golbeck/INST633o/Viz.shtml

Views: 62694
jengolbeck

SSAS - Data Mining - Decision Trees, Clustering, Neural networks

Views: 999
M R Dhandhukia

Hierarchical Clustering - Fun and Easy Machine Learning with Examples
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.
To learn more on Augmented Reality, IoT, Machine Learning FPGAs, Arduinos, PCB Design and Image Processing then Check out
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Views: 19550
Augmented Startups

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: 183
Association for Computing Machinery (ACM)

This course focuses on data analytics and machine learning techniques in MATLAB using functionality within Statistics and Machine Learning Toolbox and Neural Network Toolbox.
https://matlab4engineers.com/product/machine-learning/

Views: 988
MATLAB For Engineers

My web page:
www.imperial.ac.uk/people/n.sadawi

Views: 18272
Noureddin Sadawi

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: 445865
Victor Lavrenko

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: 19641
Esri Events

Tutorial on cluster analysis of incident points in ArcGIS 10.2 for GPH904 at Salem State University
Interested in learning more from me? Salem State University offers a Bachelor of Science in Cartography and GIS. We also offer a graduate Certificate and a Master of Science in Geo-Information Science. Learn more at https://www.salemstate.edu/academics/colleges-and-schools/college-arts-and-sciences/geography

Views: 20182
Marcos Luna

An example of how UbiGraph, NetworkX and other Python packages can be used to visualize network data in an analytically significant way.
Here, hierarchical clustering on geodesic distances is used to break a network down based on nodal clustering.

Views: 1210
agconway

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: 34734
DataCamp

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:
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Views: 27619
StatQuest with Josh Starmer

Facundo Mémoli
November 7, 2015
The problem of determining clusters in a data set admits different interpretations depending on whether the data is metric, symmetric but not necessarily metric, or asymmetric. Whereas there is a good degree of understanding of what are the natural methods for clustering symmetric data, the landscape of methods for clustering asymmetric data is not so well understood. It is possible to study and characterize hierarchical clustering methods that operate on asymmetric networks in an axiomatic manner. In the setting of symmetric data a similar axiomatic leads to a uniqueness theorem, but, in the context of asymmetric data, it turns out that all possible hierarchical clustering methods satisfying these axioms are contained, in an appropriate sense, between two extremal canonical methods. Furthermore, there exist infinite families of methods that mediate between the two extremal methods. We will describe these results and a further classification of these methods based on other properties of practical interest.
More videos at https://video.ias.edu

Views: 197
Institute for Advanced Study

Clustering creates a series of “like” groupings based on commonalities within the data. Alteryx provides a tool grouping specific to Clustering to help understand key characteristics of customers, locations or products. This video will review the various clustering algorithms within the Alteryx platform and how to determine the proper number of clusters to create logical groups that can be leveraged in additional analytical processes.

Views: 10761
Alteryx

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