Cluster analysis is an unsupervised machine learning technique that groups unlabeled data points into clusters based on similarities. It aims to divide data into meaningful groups (clusters) where items in the same cluster are more similar to each other than items in different clusters. The document discusses different types of cluster analysis methods including hierarchical agglomerative clustering which starts with each data point as its own cluster and merges them together based on similarity, and k-means clustering which partitions data into k mutually exclusive clusters where each observation belongs to the cluster with the nearest mean. It also covers topics like distance measures, selecting the optimal number of clusters, and limitations of cluster analysis techniques.
This presentation on Recurrent Neural Network will help you understand what is a neural network, what are the popular neural networks, why we need recurrent neural network, what is a recurrent neural network, how does a RNN work, what is vanishing and exploding gradient problem, what is LSTM and you will also see a use case implementation of LSTM (Long short term memory). Neural networks used in Deep Learning consists of different layers connected to each other and work on the structure and functions of the human brain. It learns from huge volumes of data and used complex algorithms to train a neural net. The recurrent neural network works on the principle of saving the output of a layer and feeding this back to the input in order to predict the output of the layer. Now lets deep dive into this presentation and understand what is RNN and how does it actually work.
Below topics are explained in this recurrent neural networks tutorial:
1. What is a neural network?
2. Popular neural networks?
3. Why recurrent neural network?
4. What is a recurrent neural network?
5. How does an RNN work?
6. Vanishing and exploding gradient problem
7. Long short term memory (LSTM)
8. Use case implementation of LSTM
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
Learn more at: https://www.simplilearn.com/
Random Forest Algorithm - Random Forest Explained | Random Forest In Machine ...Simplilearn
This Random Forest Algorithm Presentation will explain how Random Forest algorithm works in Machine Learning. By the end of this video, you will be able to understand what is Machine Learning, what is classification problem, applications of Random Forest, why we need Random Forest, how it works with simple examples and how to implement Random Forest algorithm in Python.
Below are the topics covered in this Machine Learning Presentation:
1. What is Machine Learning?
2. Applications of Random Forest
3. What is Classification?
4. Why Random Forest?
5. Random Forest and Decision Tree
6. Comparing Random Forest and Regression
7. Use case - Iris Flower Analysis
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
This presentation on Recurrent Neural Network will help you understand what is a neural network, what are the popular neural networks, why we need recurrent neural network, what is a recurrent neural network, how does a RNN work, what is vanishing and exploding gradient problem, what is LSTM and you will also see a use case implementation of LSTM (Long short term memory). Neural networks used in Deep Learning consists of different layers connected to each other and work on the structure and functions of the human brain. It learns from huge volumes of data and used complex algorithms to train a neural net. The recurrent neural network works on the principle of saving the output of a layer and feeding this back to the input in order to predict the output of the layer. Now lets deep dive into this presentation and understand what is RNN and how does it actually work.
Below topics are explained in this recurrent neural networks tutorial:
1. What is a neural network?
2. Popular neural networks?
3. Why recurrent neural network?
4. What is a recurrent neural network?
5. How does an RNN work?
6. Vanishing and exploding gradient problem
7. Long short term memory (LSTM)
8. Use case implementation of LSTM
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
Learn more at: https://www.simplilearn.com/
Random Forest Algorithm - Random Forest Explained | Random Forest In Machine ...Simplilearn
This Random Forest Algorithm Presentation will explain how Random Forest algorithm works in Machine Learning. By the end of this video, you will be able to understand what is Machine Learning, what is classification problem, applications of Random Forest, why we need Random Forest, how it works with simple examples and how to implement Random Forest algorithm in Python.
Below are the topics covered in this Machine Learning Presentation:
1. What is Machine Learning?
2. Applications of Random Forest
3. What is Classification?
4. Why Random Forest?
5. Random Forest and Decision Tree
6. Comparing Random Forest and Regression
7. Use case - Iris Flower Analysis
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
A tutorial on LDA that first builds on the intuition of the algorithm followed by a numerical example that is solved using MATLAB. This presentation is an audio-slide, which becomes self-explanatory if downloaded and viewed in slideshow mode.
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
A tutorial on LDA that first builds on the intuition of the algorithm followed by a numerical example that is solved using MATLAB. This presentation is an audio-slide, which becomes self-explanatory if downloaded and viewed in slideshow mode.
An Efficient Clustering Method for Aggregation on Data FragmentsIJMER
Clustering is an important step in the process of data analysis with applications to numerous fields. Clustering ensembles, has emerged as a powerful technique for combining different clustering results to obtain a quality cluster. Existing clustering aggregation algorithms are applied directly to large number of data points. The algorithms are inefficient if the number of data points is large. This project defines an efficient approach for clustering aggregation based on data fragments. In fragment-based approach, a data fragment is any subset of the data. To increase the efficiency of the proposed approach, the clustering aggregation can be performed directly on data fragments under comparison measure and normalized mutual information measures for clustering aggregation, enhanced clustering aggregation algorithms are described. To show the minimal computational complexity. (Agglomerative, Furthest, and Local Search); nevertheless, which increases the accuracy.
Classification of common clustering algorithm and techniques, e.g., hierarchical clustering, distance measures, K-means, Squared error, SOFM, Clustering large databases.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The method of identifying similar groups of data in a data set is called clustering. Entities in each group are comparatively more similar to entities of that group than those of the other groups.
MANAGEMENT OF ATRIOVENTRICULAR CONDUCTION BLOCK.pdfJim Jacob Roy
Cardiac conduction defects can occur due to various causes.
Atrioventricular conduction blocks ( AV blocks ) are classified into 3 types.
This document describes the acute management of AV block.
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
3. DEFINITION
• Cluster Analysis is a way of grouping cases of data
based on the similarity of responses to several
variables.
▪ The fundamental problem clustering address is to
divide the data into meaningful groups (clusters).
Group Together Variables
Grouping Cases
Factor Analysis
Cluster Analysis
4/17/2020 DR ATHAR KHAN 3
12. Unsupervised learning is a machine learning technique, where you do not need to
supervise the model. Instead, you need to allow the model to work on its own to
discover information, only have input data (X) and no corresponding output variables.4/17/2020 DR ATHAR KHAN 12
13. Types of Data
▪ The data used in cluster analysis can be interval,
ordinal or categorical.
▪ However, having a mixture of different types of
variable will make the analysis more complicated.
▪ This is because in cluster analysis you need to have
some way of measuring the distance between
observations and the type of measure used will
depend on what type of data you have.
4/17/2020 DR ATHAR KHAN 13
14. Measures of Distance
▪ A number of different measures have been proposed
to measure ’distance’ for categorical data:
▪ K-Means algorithm for categorical data, ROCK, LIMBO,
CLICKS, Ward’s agglomerativealgorithm
▪ In a hierarchical clustering algorithm most used is Ward’s.
▪ It is the most widely used method for measuring the
distance between the objects for interval data is
Euclidean Distance.
4/17/2020 DR ATHAR KHAN 14
15. Euclidean Distance, d
Euclidean distance is the geometric distance
between two objects (or cases). Therefore, if we
were to call George subject i and Zippy subject j,
then we could express their Euclidean distance in
terms of the following equation:
Euclidean distances the smaller the distance, the
more similar the cases.4/17/2020 DR ATHAR KHAN 15
16. Measures of Distance
▪ When using a measure such as the Euclidean
distance, the scale of measurement of the variables
under consideration is an issue, as changing the scale
will obviously effect the distance between subjects
(e.g. a difference of 10cm could being a difference of
100mm).
▪ To get around this problem each variable can be
standardized (converted to z-scores).
4/17/2020 DR ATHAR KHAN 16
17. Approaches to Cluster Analysis
▪ There are a number of different methods that can be
used to carry out a cluster analysis:
▪ Hierarchical methods
▪ – Agglomerative methods
▪ – Divisive methods
▪ Non-hierarchical methods (often known as k-means
clustering methods)
4/17/2020 DR ATHAR KHAN 17
18. Agglomerative Methods
▪ Agglomerative clustering is Bottom-up technique start by
considering each data point as its own cluster and
merging them together into larger groups from the
bottom up into a single giant cluster.
4/17/2020 DR ATHAR KHAN 18
19. Divisive Clustering
▪ Divisive clustering is the opposite, it starts with one
cluster, which is then divided in two as a function of the
similarities or distances in the data. These new clusters
are then divided, and so on until each case is a cluster.
Agglomerative
methods are
used more
often than
Divisive
methods
4/17/2020 DR ATHAR KHAN 19
21. Hierarchical agglomerative methods
Within this approach to cluster analysis there are a number of different
methods used to determine which clusters should be joined at each stage.
Linkage Function/Creating the Clusters
4/17/2020 DR ATHAR KHAN 21
22. Nearest neighbour method (single linkage method)
In this method the distance between two clusters is defined to be the distance
between the two closest members, or neighbours.
Furthest neighbour method (complete linkage method)
In this case the distance between two clusters is defined to be the maximum
distance between members — i.e. the distance between the two subjects that
are furthest apart.
4/17/2020 DR ATHAR KHAN 22
23. Average (between groups) linkage method (sometimes referred to as
UPGMA)
The distance between two clusters is calculated as the average distance
between all pairs of subjects in the two clusters.
Centroid Method
Here the centroid (mean value for each variable) of each cluster is calculated
and the distance between centroids is used. Clusters whose centroids are
closest together are merged.
4/17/2020 DR ATHAR KHAN 23
24. Ward’s Method
▪ In this method all possible pairs of clusters are combined and
the sum of the squared distances within each cluster is
calculated.
▪ This is then summed over all clusters.
▪ The combination that gives the lowest sum of squares is
chosen.
▪ The aim in Ward’s method is to join cases into clusters such
that the variance within a cluster is minimised.
▪ To be more precise, two clusters are merged if this merger
results in the minimum increase in the error sum of squares.
▪ Most popular Method
4/17/2020 DR ATHAR KHAN 24
25. Selecting the optimum number of clusters
▪ Once the cluster analysis has been carried out it is then necessary to
select the ’best’ cluster solution.
▪ # of clusters and within cluster variances
4/17/2020 DR ATHAR KHAN 25
26. Dendrogram
1
2
34
In the dendrogram above, the height of the
dendrogram indicates the order in which the
clusters were joined.
Dendrograms cannot tell you how many clusters
you should have4/17/2020 DR ATHAR KHAN 26
27. Data Preparation
• To perform a cluster analysis, generally, the data
should be prepared as follows:
• Any missing value in the data must be removed or
estimated.
• The data must be standardized(Z SCORES)
4/17/2020 DR ATHAR KHAN 27
28. Limitations of Cluster Analysis
• There are several things to be aware of when conducting
cluster analysis:
– The different methods of clustering usually give very different results.
This occurs because of the different criterion for merging clusters
(including cases). It is important to think carefully about which method
is best for what you are interested in looking at.
– With the exception of simple linkage, the results will be affected by
the way in which the variables are ordered.
– The analysis is not stable when cases are dropped: this occurs because
selection of a case (or merger of clusters) depends on similarity of one
case to the cluster.
4/17/2020 DR ATHAR KHAN 28
29. Limitations of Cluster Analysis
• Imagine we wanted to look at clusters of cases
referred for psychiatric treatment.
• We measured each subject on four questionnaires:
Spielberger Trait Anxiety Inventory (STAI), the Beck
Depression Inventory (BDI), a measure of Intrusive
Thoughts and Rumination (IT) and a measure of
Impulsive Thoughts and Actions (Impulse).
• The rationale behind this analysis is that people with
the same disorder should report a similar pattern of
scores across the measures (so the profiles of their
responses should be similar)
4/17/2020 DR ATHAR KHAN 29
30. Video : Hierarchical Clustering : Agglomerative Clustering and
Divisive Clustering
https://www.youtube.com/watch?v=7enWesSofhg
4/17/2020 DR ATHAR KHAN 30
36. Agglomeration schedule: Shows how the clusters are combined at each stage.
Stage 1: Cases 1 and 4 have the smallest distance ("Coefficients" = .168) => first
cluster {1,4}
Stage 2: Cases 10 and 12 have the second smallest distance => second cluster
{10,12}4/17/2020 DR ATHAR KHAN 36
38. Agglomeration schedule: Shows how the clusters are combined at each stage.
The next part of the table shows the stage at which each cluster first appears.
4/17/2020 DR ATHAR KHAN 38
39. Agglomeration schedule: Shows how the clusters are combined at each stage.
In stage 6, cluster 1 is the cluster that was formed in stage 1...
4/17/2020 DR ATHAR KHAN 39
40. Agglomeration schedule: Shows how the clusters are combined at each stage.
Stage 1: Cases 1 and 4 have the smallest distance ("Coefficients" = .168) => first cluster
{1,4}
First cluster {1,4} is merged with case 13 in stage 6 ("Next Stage") => Cluster {1,4,13}
0 means first time
4/17/2020 DR ATHAR KHAN 40
42. ▪ The Coefficients column indicates the distance between the two clusters (or
cases) joined at each stage.
▪ The values here depend on the proximity measure and linkage method used
in the analysis.
▪ For a good cluster solution, you will see a sudden jump in the distance
coefficient as you read down the table.
▪ The stage before the sudden change indicates the optimal stopping point for
merging clusters.
3 clusters
2 Clusters
1 Cluster
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43. NUMBER OF CLUSTERS
▪ Number of cases 15
▪ Step of ‘elbow’ 12
15 – 12
Number of clusters 3
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46. ▪ Notice how the "branches" merge together as you look from left to right in the
dendrogram.
▪ Cases or clusters that are joined by lines "further down" the tree (near the left side
of the dendrogram) are very similar.
The dendrogram (or "tree diagram") shows relative similarities between cases.
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47. ▪ Cases or clusters that are joined by lines "further up" the tree (near the right side)
are dissimilar.
▪ Cluster distances are rescaled so that they range from 0 to 25 in this plot.
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48. ▪ This would identify 3 clusters (GREEN), one for each point where a branch intersects
our line.
▪ By considering different cut points for our line, we can get solutions with different
numbers of cluster.
▪ A good cluster solution is one with small within-cluster distances, but large between
cluster distances.
1
2
3
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49. ▪ Choose the number of clusters within the largest increase in heterogeneity.
1
2
3
Standardized distance
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50. ▪ This table shows cluster membership for each case, according to the
number of clusters you requested.
▪ You can attempt to interpret the clusters by observing which cases are
grouped together.
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51. ▪ This table shows cluster membership for each case, according to the
number of clusters you requested.
▪ You can attempt to interpret the clusters by observing which cases are
grouped together.
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53. ▪ Having eyeballed the dendrogram and decided how many
clusters are present it is possible to re-run the analysis asking
SPSS to save a new variable in which cluster codes are assigned
to cases (with the researcher specifying the number of clusters
in the data).
▪ For these data, we saw three clear clusters and so we could re-
run the analysis asking for cluster group codings for three
clusters (in fact, I told you to do this as part of the original
analysis).
▪ The output below shows the resulting codes for each case in this
analysis. It’s pretty clear that these codes map exactly onto the
DSM-IV classifications.
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54. ▪ This table shows cluster membership for each case, according to the
number of clusters you requested.
▪ You can attempt to interpret the clusters by observing which cases are
grouped together.
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55. 4/17/2020 DR ATHAR KHAN 55
DR ATHAR KHAN
MBBS, MCPS, DPH, DCPS-HCSM, DCPS-HPE, MBA, PGD-
STATISTICS, CCRP
ASSOCIATE PROFESSOR
DEPARTMENT OF COMMUNITY MEDICINE
LIAQUAT COLLEGE OF MEDICINE & DENTISTRY
KARACHI, PAKISTAN
0092-3232135932