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Data Mining - Clustering
 
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What is clustering Partitioning a data into subclasses. Grouping similar objects. Partitioning the data based on similarity. Eg:Library. Clustering Types Partitioning Method Hierarchical Method Agglomerative Method Divisive Method Density Based Method Model based Method Constraint based Method These are clustering Methods or types. Clustering Algorithms,Clustering Applications and Examples are also Explained.
Lecture 58 — Overview of Clustering | Mining of Massive Datasets | Stanford University
 
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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. .
K mean clustering algorithm with solve example
 
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Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://goo.gl/to1yMH or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 if you have any query email us at [email protected] or [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 258756 Last moment tuitions
Data Analysis:  Clustering and Classification (Lec. 1, part 1)
 
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Supervised and unsupervised learning algorithms
Views: 59002 Nathan Kutz
Cluster Analysis | Categorization
 
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Clustering is the process of grouping the data into classes or clusters so that objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters.
Views: 4789 Red Apple Tutorials
Data Mining & Business Intelligence | Tutorial #25 | Single Linkage Clustering (Solved Problem)
 
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Order my books at 👉 http://www.tek97.com/ This video shows you how to solve a problem on Single Linkage Clustering in Data Mining for Hierarchical Clustering and also to draw a Dendogram. Watch Now ! يوضح لك هذا الفيديو كيفية حل مشكلة في تجميع الوصلات المفردة في استخراج البيانات للتسلسل الهرمي وكذلك لرسم رسم بياني Dendogram. شاهد الآن ! Este vídeo mostra como resolver um problema em clustering de ligação única em mineração de dados para cluster hierárquico e também para desenhar um dendograma. Assista agora ! Dieses Video zeigt Ihnen, wie Sie ein Problem beim Single Linkage Clustering im Data Mining für hierarchisches Clustering lösen und auch ein Dendogramm zeichnen können. Schau jetzt ! Este video muestra cómo resolver un problema en Agrupación de vinculación única en Minería de datos para Agrupación jerárquica y también para dibujar un Dendograma. Ver ahora ! На этом видео показано, как решить проблему кластеризации Single Linkage в Data Mining для иерархического кластеризации, а также для рисования Dendogram. Смотри ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 556 Ranji Raj
K-Means Clustering Algorithm - Cluster Analysis | Machine Learning Algorithm | Data Science |Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4 Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #kmeans #clusteranalysis #clustering #datascience #machinelearning How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies Please write back to us at [email protected] or call us at +918880862004 or 18002759730 for more information. Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 55407 edureka!
Data Mining, Classification, Clustering, Association Rules, Regression, Deviation
 
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Complete set of Video Lessons and Notes available only at http://www.studyyaar.com/index.php/module/20-data-warehousing-and-mining Data Mining, Classification, Clustering, Association Rules, Sequential Pattern Discovery, Regression, Deviation http://www.studyyaar.com/index.php/module-video/watch/53-data-mining
Views: 82714 StudyYaar.com
The partitioning method of clusteringSR
 
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Paper: Multivariate Analysis Module: The partitioning method of clustering Content Writer: Souvik Bandyopadhyay
Views: 4479 Vidya-mitra
Review on Clustering Techniques in Data Mining 2016
 
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Data Preprocessing in Data Mining Part one https://www.youtube.com/watch?v=cz2q_... Data Preprocessing in Data Mining Part two https://www.youtube.com/watch?v=70R_u... https://www.facebook.com/Pshtiwan.M.Aziz
Views: 255 Pshtiwan Aziz
12. Clustering
 
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag discusses clustering. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 67148 MIT OpenCourseWare
Hierarchical Clustering (Agglomerative and Divisive Clustering)
 
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My web page: www.imperial.ac.uk/people/n.sadawi
Views: 46934 Noureddin Sadawi
Hierarchical Agglomerative Clustering [HAC - Single Link]
 
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Data Warehouse and Mining For more: http://www.anuradhabhatia.com
Views: 62257 Anuradha Bhatia
K-means clustering: how it works
 
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Full lecture: http://bit.ly/K-means The K-means algorithm starts by placing K points (centroids) at random locations in space. We then perform the following steps iteratively: (1) for each instance, we assign it to a cluster with the nearest centroid, and (2) we move each centroid to the mean of the instances assigned to it. The algorithm continues until no instances change cluster membership.
Views: 446833 Victor Lavrenko
K-Means Clustering Algorithm – Solved Numerical Question 1(Euclidean Distance)(Hindi)
 
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K-Means Clustering Algorithm – Solved Numerical Question 1(Euclidean Distance)(Hindi) Data Warehouse and Data Mining Lectures in Hindi
Lecture 59 — Hierarchical Clustering | Stanford University
 
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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. .
Hierarchical Clustering - Fun and Easy Machine Learning
 
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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 http://www.arduinostartups.com/ Please like and Subscribe for more videos :)
Views: 19797 Augmented Startups
Introduction To Cluster Analysis
 
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This is short tutorial for What it is? (What do we mean by a cluster?) How it is different from decision tree? What is distance and linkage function? What is hierarchical clustering? What is scree plot & dendogram? What is non hierarchical clustering (k-means)? How to learn it in detail (step by step)? --------------------------------- Read in great detail along with Excel output, computation and SAS code ---------------------------------- https://www.udemy.com/cluster-analysis-motivation-theory-practical-application/?couponCode=FB_CA_001
Views: 126590 Gopal Malakar
Data Mining Classification and Prediction ( in Hindi)
 
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A tutorial about classification and prediction in Data Mining .
Views: 19632 Red Apple Tutorials
K-Mean Clustering
 
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Data Warehouse and Mining For more: http://www.anuradhabhatia.com
Views: 84408 Anuradha Bhatia
Data Mining Lecture -- Bayesian Classification | Naive Bayes Classifier | Solved Example (Eng-Hindi)
 
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In the bayesian classification The final ans doesn't matter in the calculation Because there is no need of value for the decision you have to simply identify which one is greater and therefore you can find the final result. -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 124214 Well Academy
Introduction to Cluster Analysis with R - an Example
 
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Provides illustration of doing cluster analysis with R. R File: https://goo.gl/BTZ9j7 Machine Learning videos: https://goo.gl/WHHqWP Includes, - Illustrates the process using utilities data - data normalization - hierarchical clustering using dendrogram - use of complete and average linkage - calculation of euclidean distance - silhouette plot - scree plot - nonhierarchical k-means clustering Cluster analysis is an important tool related to analyzing big data or working in data science field. Deep Learning: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 92226 Bharatendra Rai
DBSCAN - Density Based Clustering Method - Full technique with visual examples
 
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Here we discuss DBSCAN which is one of the method that uses Density based clustering method. Here we discuss the Algorithm, shows some examples and also give advantages and disadvantages of DBSCAN. The url of dbscan in python : http://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html
Views: 11739 Machine Learning - CTW
Data Mining & Business Intelligence | Tutorial #22 | BIRCH
 
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Order my books at 👉 http://www.tek97.com/ BIRCH is a technique used for clustering in data mining sets for scalable clusters. Watch now ! BIRCH هي تقنية تستخدم لتجميع مجموعات بيانات التعدين للمجموعات القابلة للتوسع. شاهد الآن ! BIRCH é uma técnica usada para cluster em conjuntos de mineração de dados para clusters escalonáveis. Assista agora ! BIRCH - это метод, используемый для кластеризации в наборах интеллектуального анализа данных для масштабируемых кластеров. Смотри ! BIRCH ist eine Technik, die zum Clustering in Data Mining-Sets für skalierbare Cluster verwendet wird. Schau jetzt ! BIRCH est une technique utilisée pour la mise en cluster dans des ensembles d'exploration de données pour des clusters évolutifs. Regarde maintenant ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 601 Ranji Raj
Mining of Road Accident Data Using K Means Clustering and Apriori Algorithm
 
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Introduction Road and accidents are uncertain and unsure incidents. In today’s world, traffic is increasing at a huge rate which leads to a large numbers of road accidents. Most of the road accident data analysis use data mining techniques, focusing on identifying factors that affect the severity of an accident. Association rule mining is one of the popular data mining techniques that identify the correlation in various attributes of road accident. In this project, Apriori algorithm clubbed with Kmeans Clustering is used to analyse the road accidents factors Kmeans Algorithm The algorithm is composed of the following steps: It randomly chooses K points from the data set. Then it assigns each point to the group with closest centroid. It again recalculates the centroids. Assign each point to closest centroid. The process repeats until there is no change in the position of centroids. Apriori Algorithm Apriori involves frequent item-sets, which is a set of items appearing together in the given number of database records meeting the user-specified threshold. Apriori uses a bottom-up search method that creates every single frequent item-set. This means that to produce a frequent item-set of length; it must produce all of its subsets as need to be frequent. Follow Us: Facebook : https://www.facebook.com/E2MatrixTrainingAndResearchInstitute/ Twitter: https://twitter.com/e2matrix_lab/ LinkedIn: https://www.linkedin.com/in/e2matrix-thesis-jalandhar/ Instagram: https://www.instagram.com/e2matrixresearch/
DBSCAN ( Density Based Spatial  Clustering of Application with Noise )  in Hindi | DWM | Data Mining
 
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Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://goo.gl/to1yMH or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 if you have any query email us at [email protected] or [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 10912 Last moment tuitions
Data Mining & Business Intelligence | Tutorial #26 | OPTICS
 
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Order my books at 👉 http://www.tek97.com/ OPTICS is a density based clustering technique in data mining for identifying arbitrary shaped clusters. Watch Now ! OPTICS هي تقنية تجميع تعتمد على الكثافة في التنقيب عن البيانات لتحديد المجموعات العشوائية. شاهد الآن ! ОПТИКА - это метод кластеризации на основе плотности при добыче данных для идентификации кластеров произвольной формы. Смотри ! OPTICS es una técnica de agrupación basada en la densidad en la minería de datos para identificar clusters con formas arbitrarias. Ver ahora ! OPTICS ist eine dichte-basierte Clustering-Technik im Data Mining zur Identifizierung beliebig geformter Cluster. Schau jetzt ! OPTICS est une technique de clustering basée sur la densité dans l'exploration de données pour identifier des groupes de formes arbitraires. Regarde maintenant ! OPTICS é uma técnica de clustering baseada em densidade em mineração de dados para identificar clusters de forma arbitrária. Assista agora ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 276 Ranji Raj
Kmeans Clustering Algorithm example Data Mining Centroid Based Technique part-2
 
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K means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. For more visit below links : https://en.wikipedia.org/wiki/K-means_clustering https://www.datascience.com/blog/introduction-to-k-means-clustering-algorithm-learn-data-science-tutorials ############### BOOK NAME####################### book name: techmax publications datawarehousing and mining by arti deshpande n pallavi halarnkar
Views: 17971 fun 2 code
Last minute tutorials | k mean Clustering | ADBMS
 
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NOTES:- Theory of computation : https://viden.io/knowledge/theory-of-computation?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 DAA(all topics are included in this link) : https://viden.io/knowledge/design-and-analysis-of-algorithms-topic-wise-ada?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 Advanced DBMS : https://viden.io/knowledge/advanced-dbms?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 for QM method-https://viden.io/knowledge/quine-mccluskey-method-qm-method?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 K-MAPS : https://viden.io/knowledge/k-maps-karnaugh-map?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 Basics of logic gates : https://viden.io/knowledge/basics-of-logic-gates-and-more?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 Website: https://lmtutorials.com/ Facebook: https://www.facebook.com/Last-Minute-Tutorials-862868223868621/ For any queries or suggestions, kindly mail at: [email protected]
Views: 78053 Last Minute Tutorials
Hierarchical Agglomerative Clustering [HAC - Average Link]
 
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Data Warehouse and Mining For more: http://www.anuradhabhatia.com
Views: 16130 Anuradha Bhatia
Mod-01 Lec-26 Cluster Analysis
 
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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: 36222 nptelhrd
Lecture 60 — The k Means Algorithm | Stanford University
 
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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. .
More Data Mining with Weka (3.5: Representing clusters)
 
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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: 47062 WekaMOOC
Spatial Data Mining I: Essentials of Cluster Analysis
 
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Whenever we look at a map, it is natural for us to organize, group, differentiate, and cluster what we see to help us make better sense of it. This session will explore the powerful Spatial Statistics techniques designed to do just that: Hot Spot Analysis and Cluster and Outlier Analysis. We will demonstrate how these techniques work and how they can be used to identify significant patterns in our data. We will explore the different questions that each tool can answer, best practices for running the tools, and strategies for interpreting and sharing results. This comprehensive introduction to cluster analysis will prepare you with the knowledge necessary to turn your spatial data into useful information for better decision making.
Views: 19886 Esri Events
Mod-01 Lec-04 Clustering vs. Classification
 
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Pattern Recognition by Prof. C.A. Murthy & Prof. Sukhendu Das,Department of Computer Science and Engineering,IIT Madras.For more details on NPTEL visit http://nptel.ac.in
Views: 19676 nptelhrd
Crime Data Analysis Using Kmeans Clustering Technique
 
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Introduction Data Mining deals with the discovery of hidden knowledge, unexpected patterns and new rules from large databases. Crime analyses is one of the important application of data mining. Data mining contains many tasks and techniques including Classification, Association, Clustering, Prediction each of them has its own importance and applications It can help the analysts to identify crimes faster and help to make faster decisions. The main objective of crime analysis is to find the meaningful information from large amount of data and disseminates this information to officers and investigators in the field to assist in their efforts to apprehend criminals and suppress criminal activity. In this project, Kmeans Clustering is used for crime data analysis. Kmeans Algorithm The algorithm is composed of the following steps: It randomly chooses K points from the data set. Then it assigns each point to the group with closest centroid. It again recalculates the centroids. Assign each point to closest centroid. The process repeats until there is no change in the position of centroids. Example of KMEANS Algorithm Let’s imagine we have 5 objects (say 5 people) and for each of them we know two features (height and weight). We want to group them into k=2 clusters. Our dataset will look like this: First of all, we have to initialize the value of the centroids for our clusters. For instance, let’s choose Person 2 and Person 3 as the two centroids c1 and c2, so that c1=(120,32) and c2=(113,33). Now we compute the Euclidean distance between each of the two centroids and each point in the data.
Data Mining & Business Intelligence | Tutorial # 24 | DBSCAN
 
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Order my books at 👉 http://www.tek97.com/ DBSCAN is a density based clustering technique that focuses on the density parameters. Watch Now ! DBSCAN هي تقنية تجميع تعتمد على الكثافة التي تركز على معلمات الكثافة. شاهد الآن ! DBSCAN es una técnica de agrupación basada en la densidad que se centra en los parámetros de densidad. Ver ahora ! DBSCAN - это метод кластеризации на основе плотности, который фокусируется на параметрах плотности. Смотри ! DBSCAN est une technique de regroupement basée sur la densité qui se concentre sur les paramètres de densité. Regarde maintenant ! DBSCAN ist eine dichtebasierte Clustering-Technik, die sich auf die Dichteparameter konzentriert. Schau jetzt ! O DBSCAN é uma técnica de clustering baseada em densidade que enfoca os parâmetros de densidade. Assista agora ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 275 Ranji Raj
Data Mining : Clustering Algorithm By Dunk Stat 43
 
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Views: 1437 Chawannut Prommin

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