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CLIQUE Automatic subspace clustering of high dimensional data for data mining...
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محاضرة ألقيتها ضمن برنامج السيمينار الذي نفذه قسم علوم الحاسوب وتكنولوجيا المعلومات في الكلية الجامعية للعلوم والتكنولوجيا عام 2012
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** 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
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** 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...
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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 Algorithm | K Means Clustering Example | Machine Learning ...
K Means Clustering Algorithm | K Means Clustering Example | Machine Learning ...
Simplilearn
https://telecombcn-dl.github.io/2018-dlai/ Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Optimization for Neural Network Training - Veronica Vilaplana - UPC Barcelona...
Optimization for Neural Network Training - Veronica Vilaplana - UPC Barcelona...
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butest
This presentation about hierarchical clustering will help you understand what is clustering, what is hierarchical clustering, how does hierarchical clustering work, what is distance measure, what is agglomerative clustering, what is divisive clustering and you will also see a demo on how to group states based on their sales using clustering method. Clustering is the method of dividing the objects into clusters which are similar between them and are dissimilar to the objects belonging to another cluster. It is used to find data clusters such that each cluster has the most closely matched data. Prototype-based clustering, hierarchical clustering, and density-based clustering are the three types of clustering algorithms. Lets us discuss hierarchical clustering in this video. In simple terms, Hierarchical clustering is separating data into different groups based on some measure of similarity. Below topics are explained in this "Hierarchical Clustering" presentation: 1. What is clustering? 2. What is hierarchical clustering 3. How hierarchical clustering works? 4. Distance measure 5. What is agglomerative clustering 6. What is divisive clustering 7. Demo: to group states based on their sales 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 We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning Learn more at www.simplilearn.com
Hierarchical Clustering | Hierarchical Clustering in R |Hierarchical Clusteri...
Hierarchical Clustering | Hierarchical Clustering in R |Hierarchical Clusteri...
Simplilearn
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Unsupervised learning: Clustering
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Classification Based Machine Learning Algorithms
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Md. Main Uddin Rony
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Machine learning clustering
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CosmoAIMS Bassett
These are the slides for a tutorial talk about "multilayer networks" that I gave at NetSci 2014. I walk people through a review article that I wrote with my PLEXMATH collaborators: http://comnet.oxfordjournals.org/content/2/3/203
Multilayer tutorial-netsci2014-slightlyupdated
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Mason Porter
the 5th social media course at skema business school about social network analysis
Social network analysis course 2010 - 2011
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guillaume ereteo
A Data Mining Paper Presentation on Classification
SLIQ
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Sara Alaee
Machine Learning: Generative and Discriminative Models
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butest
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K Means Clustering Algorithm | K Means Example in Python | Machine Learning A...
K Means Clustering Algorithm | K Means Example in Python | Machine Learning A...
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My8clst
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Introduction to Clustering algorithm
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Machine Learning Clustering
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05 Clustering in Data Mining
05 Clustering in Data Mining
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K means Clustering
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Hierarchical Clustering | Hierarchical Clustering in R |Hierarchical Clusteri...
Unsupervised learning: Clustering
Unsupervised learning: Clustering
Classification Based Machine Learning Algorithms
Classification Based Machine Learning Algorithms
Machine learning clustering
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Multilayer tutorial-netsci2014-slightlyupdated
Multilayer tutorial-netsci2014-slightlyupdated
Social network analysis course 2010 - 2011
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SLIQ
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Machine Learning: Generative and Discriminative Models
Machine Learning: Generative and Discriminative Models
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Graph Based Clustering
En vedette
An introduction in the world of Social Network Analysis and a view on how this may help learning networks. History, data collection and several analysis techniques are shown.
The Basics of Social Network Analysis
The Basics of Social Network Analysis
Rory Sie
Approximation algorithms for clique transversals on some graph classes
Approximation algorithms for clique transversals on some graph classes
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政謙 陳
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The detection of communities in social networks is a challenging task. A rigorous way to model communities considers maximal cliques, that is, maximal subgraphs in which each pair of nodes is connected by an edge. State-of-the-art strategies for finding maximal cliques in very large networks decompose the network in blocks and then perform a distributed computation. These approaches exhibit a trade-off between efficiency and completeness: decreasing the size of the blocks has been shown to improve efficiency but some cliques may remain undetected since high-degree nodes, also called hubs, may not fit with all their neighborhood into a small block. In this paper, we present a distributed approach that, by suitably handling hub nodes, is able to detect maximal cliques in large networks meeting both completeness and efficiency. The approach relies on a two-level decomposition process. The first level aims at recursively identifying and isolating tractable portions of the network. The second level further decomposes the tractable portions into small blocks. We demonstrate that this process is able to correctly detect all maximal cliques, provided that the sparsity of the network is bounded, as it is the case of real-world social networks. An extensive campaign of experiments confirms the effectiveness, efficiency, and scalability of our solution and shows that, if hub nodes were neglected, significant cliques would be undetected.
Finding All Maximal Cliques in Very Large Social Networks
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Graph coloring has many applications, including in VLSI CAD. Since graph coloring is NP-complete, heuristics are used to approximate the optimum solution. But heuristic solutions can be arbitrary larger than the minimum coloring. We demonstrate how a greedy coloring, together with a heuristics max-clique algorithm, can be combined to generate a new pruning technique, the q-color pruning algorithm. We show that since real-life graphs appear to be 1-perfect, one can solve graph coloring exactly for a small overhead.
Exact coloring of real-life graphs is easy
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Olivier Coudert
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suicide ideation of individuals in online social networks
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The Basics of Social Network Analysis
The Basics of Social Network Analysis
Approximation algorithms for clique transversals on some graph classes
Approximation algorithms for clique transversals on some graph classes
Open Horizontal Platform - Web Scale Interoperability for the Internet of Thi...
Open Horizontal Platform - Web Scale Interoperability for the Internet of Thi...
Finding All Maximal Cliques in Very Large Social Networks
Finding All Maximal Cliques in Very Large Social Networks
Accordion - VLDB 2014
Accordion - VLDB 2014
XXL Graph Algorithms__HadoopSummit2010
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Importance
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6 Concor
6 Concor
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1 Mechanics
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6 Concor
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3 Centrality
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Suicide ideation of individuals in online social networks tokyo webmining
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4 Cliques Clusters
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Cliques, Clans and
Clusters Finding Cohesive Subgroups in Network Data
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Reciprocity
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