K Means Clustering Algorithm | K Means Example in Python | Machine Learning A...Edureka!
** Python Training for Data Science: https://www.edureka.co/python **
This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) series presents another video on "K-Means Clustering Algorithm". Within the video you will learn the concepts of K-Means clustering and its implementation using python. Below are the topics covered in today's session:
1. What is Clustering?
2. Types of Clustering
3. What is K-Means Clustering?
4. How does a K-Means Algorithm works?
5. K-Means Clustering Using Python
Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
K Means Clustering Algorithm | K Means Example in Python | Machine Learning A...Edureka!
** Python Training for Data Science: https://www.edureka.co/python **
This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) series presents another video on "K-Means Clustering Algorithm". Within the video you will learn the concepts of K-Means clustering and its implementation using python. Below are the topics covered in today's session:
1. What is Clustering?
2. Types of Clustering
3. What is K-Means Clustering?
4. How does a K-Means Algorithm works?
5. K-Means Clustering Using Python
Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
K Means Clustering Algorithm | K Means Clustering Example | Machine Learning ...Simplilearn
This K-Means clustering algorithm presentation will take you through the machine learning introduction, types of clustering algorithms, k-means clustering, how does K-Means clustering work and at least explains K-Means clustering by taking a real life use case. This Machine Learning algorithm tutorial video is ideal for beginners to learn how K-Means clustering work.
Below topics are covered in this K-Means Clustering Algorithm presentation:
1. Types of Machine Learning?
2. What is K-Means Clustering?
3. Applications of K-Means Clustering
4. Common distance measure
5. How does K-Means Clustering work?
6. K-Means Clustering Algorithm
7. Demo: k-Means Clustering
8. Use case: Color compression
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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
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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.
This is very simple introduction to Clustering with some real world example. At the end of lecture I use stackOverflow API to test some clustering. I also wants to try facebook but it has some problem with it's API
K Means Clustering Algorithm | K Means Clustering Example | Machine Learning ...Simplilearn
This K-Means clustering algorithm presentation will take you through the machine learning introduction, types of clustering algorithms, k-means clustering, how does K-Means clustering work and at least explains K-Means clustering by taking a real life use case. This Machine Learning algorithm tutorial video is ideal for beginners to learn how K-Means clustering work.
Below topics are covered in this K-Means Clustering Algorithm presentation:
1. Types of Machine Learning?
2. What is K-Means Clustering?
3. Applications of K-Means Clustering
4. Common distance measure
5. How does K-Means Clustering work?
6. K-Means Clustering Algorithm
7. Demo: k-Means Clustering
8. Use case: Color compression
- - - - - - - -
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
- - - - - - -
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.
This is very simple introduction to Clustering with some real world example. At the end of lecture I use stackOverflow API to test some clustering. I also wants to try facebook but it has some problem with it's API
Unsupervised learning Algorithms and Assumptionsrefedey275
Topics :
Introduction to unsupervised learning
Unsupervised learning Algorithms and Assumptions
K-Means algorithm – introduction
Implementation of K-means algorithm
Hierarchical Clustering – need and importance of hierarchical clustering
Agglomerative Hierarchical Clustering
Working of dendrogram
Steps for implementation of AHC using Python
Gaussian Mixture Models – Introduction, importance and need of the model
Normal , Gaussian distribution
Implementation of Gaussian mixture model
Understand the different distance metrics used in clustering
Euclidean, Manhattan, Cosine, Mahala Nobis
Features of a Cluster – Labels, Centroids, Inertia, Eigen vectors and Eigen values
Principal component analysis
Supervised learning (classification)
Supervision: The training data (observations, measurements, etc.) are accompanied by labels indicating the class of the observations
New data is classified based on the training set
Unsupervised learning (clustering)
The class labels of training data is unknown
Given a set of measurements, observations, etc. with the aim of establishing the existence of classes or clusters in the data
Types of Hierarchical Clustering
There are mainly two types of hierarchical clustering:
Agglomerative hierarchical clustering
Divisive Hierarchical clustering
A distribution in statistics is a function that shows the possible values for a variable and how often they occur.
In probability theory and statistics, the Normal Distribution, also called the Gaussian Distribution.
is the most significant continuous probability distribution.
Sometimes it is also called a bell curve.
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.
Chapter 10. Cluster Analysis Basic Concepts and Methods.pptSubrata Kumer Paul
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
2. Information:
What is Clustering?
• Clustering is alternatively called as “grouping”
• Organizing data into class such that:
High intra-class Similarity
Low inter-class Similarity
Clustering Algorithm:
• Assigning same labels to data points that are close to each
other.
• Clustering algorithms rely on a distance metric between data
points
3. Information:
(contd.)
Types Of Clustering:
1. Hierarchical: [find Successive Cluster]
Agglomerative (bottom-up)
Divisive (top-down)
2. Partitional: [Construct various partition and evaluate]
K-means Clustering
Fuzzy c-means
QT clustering
The centroid is (typically) the mean of the points in the cluster.
Similarity is measured by Euclidean distance, Manhattan
Distance
NOTE: UsedWhen data is numeric not when categorical or boolean.
4. PseudoCode:
Input: K, Set of points X1....Xn
Place Centroids C1.....Ck at random Locations
Repeat until Convergence
-for each point X1:
Find nearest Centroid Cj
Assign the point Xi to cluster j
-for each cluster j=1.....k
New Centroid Cj = mean of all points Xi assigned
to cluster j in previous step
Stop when none of the cluster assignment changes
Euclidian Distance
7. Working:
(contd.) As no points change cluster Algorithm stops
Facts
K-means is blazingly fast compared to
other Clustering Algorithm
This Algorithm is also used to form clusters in 3D
8. Application:
Real Life:
1. Marketing: Help marketers discover distinct
groups in their customer bases.
2. Insurance: Identifying groups of motor insurance
policy holders with a high average claim cost
Application Domain:
1. Vector quantization: For color quantization to
reduce the color palette of an image to a fixed
number of colors k.
2. Image Segmentation: It is the process of
partitioning a digital image into multiple
segments
9. Complexity:
O(t*k*n)
where n is # data points, k is # clusters, and
t is # iterations.
Normally, k, t << n.
Strengths:
Relatively Efficient and fast.
Often terminates Early.