Search results “Results of cluster analysis”

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

Views: 93376
James Gaskin

In this video I show how to conduct a k-means cluster analysis in SPSS, and then how to use a saved cluster membership number to do an ANOVA

Views: 57231
James Gaskin

What is CLUSTER ANALYSIS? What does CLUSTER ANALYSIS mean? CLUSTER ANALYSIS meaning - CLUSTER ANALYSIS definition - CLUSTER ANALYSIS explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics.
Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter settings (including values such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It is often necessary to modify data preprocessing and model parameters until the result achieves the desired properties.
Besides the term clustering, there are a number of terms with similar meanings, including automatic classification, numerical taxonomy, botryology (from Greek ß????? "grape") and typological analysis. The subtle differences are often in the usage of the results: while in data mining, the resulting groups are the matter of interest, in automatic classification the resulting discriminative power is of interest. This often leads to misunderstandings between researchers coming from the fields of data mining and machine learning, since they use the same terms and often the same algorithms, but have different goals.
Cluster analysis was originated in anthropology by Driver and Kroeber in 1932 and introduced to psychology by Zubin in 1938 and Robert Tryon in 1939 and famously used by Cattell beginning in 1943 for trait theory classification in personality psychology.

Views: 5264
The Audiopedia

In this video I show how to determine the most appropriate number of clusters based on the agglomeration schedule in a hierarchical cluster analysis.

Views: 11170
James Gaskin

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)?
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Read in great detail along with Excel output, computation and SAS code
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https://www.udemy.com/cluster-analysis-motivation-theory-practical-application/?couponCode=FB_CA_001

Views: 122186
Gopal Malakar

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: 86074
Bharatendra Rai

PyData London 2016
There are many different methods for finding groups in data (cluster analysis), and on many datasets they will deliver different results. How good a clustering is for given data depends on the aim of clustering. I will present a number of methods that can be used to assess the quality of a clustering and to compare different clusterings, taking into account different aims of clustering.
There are many different methods for finding groups in data (cluster analysis), and on many datasets they will deliver different results. How good a clustering is for given data depends on the aim of clustering and on the user's concept of what makes objects "belong together". I will present some approaches to assess the quality of a clustering and to compare different clusterings. Particularly, I will present some indexes that measure various desirable aspects of a clustering such as stability, separateness of clusters etc. Different aims of clustering can be taken into account by specifying which aspects are particularly relevant in the situation at hand.
Slides available here: http://www.slideshare.net/PyData/christian-henning-assessing-the-quality-of-a-clustering

Views: 1882
PyData

In this video I go over how to perform k-means clustering using r statistical computing. Clustering analysis is performed and the results are interpreted. http://www.influxity.com

Views: 178384
Influxity

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: 70428
Siraj Raval

***Sorry about the issues with audio - somehow my mic was being funny***
In this video, I briefly speak about different clustering techniques and show how to run them in SPSS. Sorry again about the audio - I'm feeling too lazy to re-record.

Views: 106101
StatArena

How to perform clustering with FactoMineR?
How to consolidate the clustering?
And how can we describe the clusters?

Views: 2796
François Husson

Australian Weather Data:
http://www.bom.gov.au/climate/dwo/

Views: 2780
Ritvik Kharkar

Views: 3160
Marketing research and analysis

Currell: Scientific Data Analysis. Minitab and SPSS analysis for Fig 9.2 http://ukcatalogue.oup.com/product/9780198712541.do © Oxford University Press

Views: 14953
Oxford Academic (Oxford University Press)

Shows students how to interpret their results from the Cluster Finder

Views: 19
janelleb5

Links of data set used in the video:
https://drive.google.com/open?id=1G4X5oEX-eo8jFa-F1QZ9ApaVEx-Dsf_I

Views: 2796
Dr. Shailesh Kaushal

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: 15871
Esri Events

Using R to do cluster analysis and display the results in various ways.

Views: 6515
Keith McGuinness

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: 75420
MktgStudyGuide

In this video, you will learn how to perform K Means Clustering using R. Clustering is an unsupervised learning algorithm.
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Views: 32159
Analytics University

Please visit http://web.ics.purdue.edu/~jinsuh/analyticspractice-cluster.php for data and sas codes.

Views: 9099
Jinsuh Lee

In this video I show how to validate a two-step cluster analysis using the AIC measure of model fit. I also discuss when to use the Euclidean distance versus the Log-likelihood distance.

Views: 5014
James Gaskin

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: 419586
Victor Lavrenko

http://www.NeuroXL.com This demo shows an example of using NeuroXL Clusterizer in financial services marketing clustering of households by their banking status)

Views: 34375
NeuroXL

This video demonstrates how to conduct a K-Means Cluster Analysis in SPSS. A K-Means Cluster Analysis allows the division of items into clusters based on specified variables.

Views: 17943
Todd Grande

Learn the basics of Cluster Analysis using real-life examples. Know more about the objective of cluster analysis, the methodology used and interpreting results from the same.
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Views: 7498
Great Learning

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.
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Views: 16195
Augmented Startups

Learn More: https://goo.gl/DszCyk
A tutorial on Cluster Analysis using real life examples. Learn the objective of cluster analysis, the methodology used and interpreting results from the same.
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Cluster analysis is an exploratory data analysis tool which aims at sorting different objects into groups in a way that the degree of association between two objects is maximal if they belong to the same group and minimal otherwise.
Clustering methods can be classified into the following categories −
Partitioning Method
Hierarchical Clustering
Density-based Method
Grid-Based Method
Model-Based Method
Constraint-based Method

Views: 28387
Great Learning

[http://bit.ly/s-link] Agglomerative clustering guarantees that similar instances end up in the same cluster. We start by having each instance being in its own singleton cluster, then iteratively do the following steps: (1) find a pair or most similar clusters and (2) merge them into a single cluster. The result is a tree structure called the dendrogram.

Views: 88301
Victor Lavrenko

Clustering resources on a map by geographic proximity

Views: 242
Seventyeight Digital

In this video Jarlath Quinn explains what cluster analysis is, how it is applied in the real world and how easy it is create your own cluster analysis models in SPSS Statistics. The video includes:
A demonstration of cluster analysis using sample data
How to use the Cluster Viewer facility to interpret and make sense of the analysis results
How to apply a cluster model to a data file and rename the groups to make them meaningful to non-experts
How use cluster analysis to illustrate how a customer base changes over time

Views: 2264
Smart Vision Europe

www.Stats-Lab.com | Data analysis with R | Hierarchical Clustering with R

Views: 18086
Dragonfly Statistics

Example data from https://www.youtube.com/playlist?list=PLIO9zcmAtJoqre_xurVP3fwYFRI35SAEH

Views: 326
Robert Lewis

kobriendublin.wordpress.com
SPSS : K Means Clustering

Views: 56969
Dragonfly Statistics

Currell: Scientific Data Analysis. Minitab analysis for Figs 9.6 and 9.7 http://ukcatalogue.oup.com/product/9780198712541.do
© Oxford University Press

Views: 253127
Oxford Academic (Oxford University Press)

Cluster analysis of low birth weight events near Shreveport, LA. Points are geomasked to protect confidentiality. The points are no longer in their original location.

Views: 1761
Ryan Bilbo

This video tutorial shows you how to use the means function in R to do K-Means clustering. You will need to know how to read in data, subset data and plot items in order to use this video

Views: 40144
Ed Boone

This video demonstrates how interpret the SPSS output for a factor analysis. Results including communalities, KMO and Bartlett’s Test, total variance explained, and the rotated component matrix are interpreted.

Views: 97314
Todd Grande

This video explains about performing Cluster Analysis with K Mean Cluster Method using SPSS.

Views: 3167
My Easy Statistics

So far you've just been learning about how to deal with the power of the time-frequency results. This video will introduce you to working with phase values, which reflect the timing of the activity relative to the time=0 event (e.g., stimulus onset).
This video uses the following MATLAB code:
http://mikexcohen.com/lecturelets/itpc/itpc.m
http://mikexcohen.com/lecturelets/sampleEEGdata.mat
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 1063
Mike X Cohen

The idea for this is from by Vidisha Vachharajani (Freelance Statistical Consultant). Using heatmap.2 to cluster variables and samples and display the results with a heatmap.

Views: 3838
Keith McGuinness

Tutorial on how to apply K-Means using Weka on a data set

Views: 5719
Jyothi Rao

This showcase demonstrates how a customer can identify “good” customers vs. “bad” customers, the characteristics of the customers, and how to visualize the results of a campaign, and the conversion probability of leads. This is accomplished through k-means clustering analysis, R predictive analytics algorithms, and Power BI.

Views: 987
Alex Ng

Let’s go through an example of how we can use Tableau’s clustering feature with some sample data. We can use Tableau’s Super Store dataset.
Let’s drag in Sales & Profit measures into the view, and then go to Analysis Tab and uncheck the “Aggregate Measures” option. This will disaggregate the values and provide us with a scatterplot of the sales & profit data.
Now switch over the Analytics Pane, and drag the “Cluster” model into the view (you can just drop it into the chart). You now see a view that shows the variables and then you are prompted to select the number of clusters you wish to create. The variables that are brought in are the ones currently in the view (sales & profit). You can drag in additional variables and you can also remove the existing default variables. Let’s put in the number 3 for the number of clusters. You can already see that the scatterplot has been colored and divided into 3 groups or segments. We also have a new color legend that is labeled with Cluster 1, Cluster 2, and Cluster 3. Now let’s close the window by pressing the X and analyze what we have created. Note that we can format the view to change the colors of the clusters and we can also rename the clusters into whatever we want to call them.
Describe that Cluster!
If you want to dig deeper to see what’s under the hood of the clusters you simply go over to the Marks area, drop down on Clusters and click on “Describe clusters”. Here you can see all of the details including the variables, level of detail, scaling, number of clusters, number of points, the between-group sum of squares, within-group sum of squares, and total sum of squares. We also get the breakdown of the number of items in each cluster (as well as the Not Clustered category; in this case that is 0). We can see the centers for sum of profit and sum of sales variables. This information is under the Summary Tab. We can also check out the Models Tab to see the ANOVA or the analysis of variance details.
What values get assigned to the Not Clustered category?
When there are null values for a measure, Tableau assigns values for rows with “null” to a “Not Clustered” category. Categorical variables that return * for ATTR (meaning that all values are not identical) are also not clustered.
A Map Example
You can also use the cluster analysis feature in a map view. Let’s create a map view of profit by location (using city as the location). Again, we will use the Analytics Pane and drag the “Cluster” model into the view by dragging and dropping. We see that the variable used for segmenting the view into clusters is profit. We can use the automatic number of clusters suggested here (which is five).
Describe that Cluster!
As we did with the first example, let’s examine what the cluster is doing. We have a total of five clusters and for each one we can see the center values and the details around sum of squares.
Summary
By playing with the clustering function you can explore patterns in the data that would be difficult to pick out with the naked eye. Tableau’s cluster analysis function allows the user to continually add more variables until the data is sorted in a meaningful way. In addition to numerical fields, the function can support categorical fields.
Resources:
https://en.wikipedia.org/wiki/Cluster_analysis
https://www.dataentryoutsourced.com/blog/how-businesses-can-use-clustering-in-data-mining/
https://www.tutorialspoint.com/data_mining/dm_cluster_analysis.htm
https://boraberan.wordpress.com/2016/07/19/understanding-clustering-in-tableau-10/
https://onlinehelp.tableau.com/current/pro/desktop/en-us/clustering_howitworks.html
http://stats.stackexchange.com/questions/133656/how-to-understand-the-drawbacks-of-k-means

Views: 4197
Story by Data

Created by: Maurya Nagaraja
Created For: ISM 6136.022F15 Coursework
Under guidance from: Dr.Balaji Padmanabhan.
Management Information Systems - (ISDS Department)
University of South Florida - Tampa

Views: 26841
200fever

This webinar discusses the use of cluster analysis for dividing a set of objects into groups. Creation of the groups is based on the values of multiple quantitative variables. Seven methods are covered, including nearest neighbor clustering, Ward’s method, and the method of k-means. The results of the clustering are presented as dendrograms, icicle plots, agglomeration distance plots, coded scatterplots, and colored maps.

Views: 606
Statgraphics

1. Summarizing average columns to get maximum read count for all groups
2. Filter for read count and p-value
3. Create list from above filter
4. Hierarchical clustering using above list
4a. Change SampleIDs from clustering
5. KMeans clustering from above list

Views: 1495
OmicsoftCorporation

More Data Mining with Weka: online course from the University of Waikato
Class 3 - Lesson 5: Representing clusters
http://weka.waikato.ac.nz/
Slides (PDF):
http://goo.gl/nK6fTv
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 45257
WekaMOOC

Applied Multivariate Analysis by Dr. Amit Mitra,Dr. Sharmishtha Mitra, Department of Mathematics and Science, IIT Kanpur. For more details on NPTEL visit http://nptel.iitm.ac.in

Views: 35886
nptelhrd

© 2018 Market leader intermediate business english course book david cotton

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