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Data Mining, Лекция №2
 
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Техносфера Mail.ru Group, МГУ им. М.В. Ломоносова. Курс "Алгоритмы интеллектуальной обработки больших объемов данных", Лекция №2 "Задача кластеризации и ЕМ-алгоритм" Лектор - Николай Анохин Постановка задачи кластеризации. Функции расстояния. Критерии качества кластеризации. EM-алгоритм. K-means и модификации. Слайды лекции http://www.slideshare.net/Technosphere1/lecture-2-47107553 Другие лекции курса Data Mining | https://www.youtube.com/playlist?list=PLrCZzMib1e9pyyrqknouMZbIPf4l3CwUP Наш видеоканал | http://www.youtube.com/user/TPMGTU?sub_confirmation=1 Официальный сайт Технопарка | https://tech-mail.ru/ Официальный сайт Техносферы | https://sfera-mail.ru/ Технопарк в ВКонтакте | http://vk.com/tpmailru Техносфера в ВКонтакте | https://vk.com/tsmailru Блог на Хабре | http://habrahabr.ru/company/mailru/ #ТЕХНОПАРК #ТЕХНОСФЕРА x
Borderlands 2 - DATA MINING - Gameplay Walkthrough - Part 35 (Xbox 360/PS3/PC) [HD]
 
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Borderlands 2 Gameplay Walkthrough Part 35 with HD Xbox 360, PS3 and PC by theRadBrad. Part 35 of this Borderlands 2 Gameplay Walkthrough includes the Mission: Data Mining. This Borderlands 2 Gameplay Walkthrough will include a Review, Gameplay and the Ending. Borderlands 2 Gameplay Walkthrough Part 1: https://www.youtube.com/watch?v=LzGYm7H3tig Borderlands 2 Playlist: http://bit.ly/TNM8sj Subscribe: http://bit.ly/vTkZzS Follow me on Twitter: http://twitter.com/thaRadBrad Like me on Facebook: http://www.facebook.com/theRadBrad T-Shirts: http://theradbrad.spreadshirt.com/ Set five years after the events of Borderlands, Handsome Jack, the game's main antagonist, has taken over the Hyperion Corporation, declared himself Dictator of Pandora and taken all of the credit for finding the Vault, going so far to claim responsibility for killing the Destroyer. Jack has also blotted out much of the light on the planet by having a giant orbiting H-shaped base set in front of Pandora's stationary moon. The new team in Borderlands 2 is tasked with killing Jack and returning peace to Pandora. Borderlands 2 begins with the protagonists on a train to an unspecified location to begin their search for the vault, only for it to turn out as a trap by Handsome Jack to kill all who search for the Vault. The Vault hunters defend themselves only for the train to crash, with them waking up in an arctic wasteland along with Claptrap. The mysterious Guardian Angel then contacts them and explains that Handsome Jack must be killed, directing players to rescue the four original Vault hunters from Hyperion's clutches to accomplish this. Borderlands 2 initially has four player characters: Axton (Commando), Maya (Siren), Salvador (Gunzerker), Zer0 (Assassin), DLC characters: Gaige (Mechromancer). Borderlands 2 NPCs include: Brick, Claptrap, Dr. Zed, Ellie, Lilith, Mad Moxxi, Marcus Kincaid, Mordecai, Patricia, Tannis, Roland, Scooter, Sir Hammerlock, Tiny Tina, Crazy Earl.
Views: 112427 theRadBrad
Data Mining: Die intelligente Nutzung von Big Data
 
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Data Mining ist die Technik um strategische Informationen aus Big Data zu extrahieren. eoda Chief Data Scientist Oliver Bracht erklärt die Grundlagen des Data Mining und das Vorgehen anhand von branchenübergreifenden Use Cases aus Industrie, Vertrieb und Marketing. Darüber hinaus gibt Bracht Handlungsempfehlungen für Unternehmen, die Big Data für sich nutzen wollen. Das Video ist entstanden im Rahmen eines Vortrags bei der German Graduate School of Management and Law.
Views: 10415 eoda GmbH
4. Data Mining. Методы снижения размерности 2
 
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Техносфера Mail.ru Group, МГУ им. М.В. Ломоносова. Курс "Методы обработки больших объемов данных" (осень 2015) Лекция №4 - "Методы снижения размерности. Линейные методы выделения признаков" Лектор - Владимир Гулин Другие лекции курса | https://www.youtube.com/playlist?list=PLrCZzMib1e9pXgyJ8Y9Io4AocGy66pj1X -- Официальный канал образовательных проектов Mail.Ru Group | http://www.youtube.com/user/TPMGTU?sub_confirmation=1 НАШИ ПРОЕКТЫ: "Технопарк" при МГТУ им. Баумана | https://park.mail.ru/ "Техносфера" при МГУ им. Ломоносова | https://sphere.mail.ru/ "Технотрек" при МФТИ | https://track.mail.ru/ Мы готовим квалифицированных специалистов для российского рынка веб-разработки. У нас - бесплатное практико-ориентированное обучение под руководством лучших специалистов Mail.Ru Group. Преподавание строится на примерах из реальной практики, существующих проектов, с анализом их достоинств и недостатков. Лучшие студенты получают возможность стажировки в Mail.Ru Group. Отбор в проекты проходит каждые полгода. МЫ В СОЦ. СЕТЯХ: Технопарк в ВКонтакте | http://vk.com/tpmailru Техносфера в ВКонтакте | https://vk.com/tsmailru Технотрек в ВКонтакте | https://vk.com/trackmailru Блог на Хабре | http://habrahabr.ru/company/mailru/
How data mining works
 
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In this video we describe data mining, in the context of knowledge discovery in databases. More videos on classification algorithms can be found at https://www.youtube.com/playlist?list=PLXMKI02h3_qjYoX-f8uKrcGqYmaqdAtq5 Please subscribe to my channel, and share this video with your peers!
Views: 229759 Thales Sehn Körting
2. Data Mining. Алгоритмические композиции. Кульминация и развязка.
 
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Техносфера Mail.ru Group, МГУ им. М.В. Ломоносова. Курс "Методы обработки больших объемов данных" (осень 2015) Лекция №2 - "Алгоритмические композиции. Кульминация и развязка." Лектор - Владимир Гулин Другие лекции курса | https://www.youtube.com/playlist?list=PLrCZzMib1e9pXgyJ8Y9Io4AocGy66pj1X -- Официальный канал образовательных проектов Mail.Ru Group | http://www.youtube.com/user/TPMGTU?sub_confirmation=1 НАШИ ПРОЕКТЫ: "Технопарк" при МГТУ им. Баумана | https://park.mail.ru/ "Техносфера" при МГУ им. Ломоносова | https://sphere.mail.ru/ "Технотрек" при МФТИ | https://track.mail.ru/ Мы готовим квалифицированных специалистов для российского рынка веб-разработки. У нас - бесплатное практико-ориентированное обучение под руководством лучших специалистов Mail.Ru Group. Преподавание строится на примерах из реальной практики, существующих проектов, с анализом их достоинств и недостатков. Лучшие студенты получают возможность стажировки в Mail.Ru Group. Отбор в проекты проходит каждые полгода. МЫ В СОЦ. СЕТЯХ: Технопарк в ВКонтакте | http://vk.com/tpmailru Техносфера в ВКонтакте | https://vk.com/tsmailru Технотрек в ВКонтакте | https://vk.com/trackmailru Блог на Хабре | http://habrahabr.ru/company/mailru/
Spatial Data Mining II: A Deep Dive into Space-Time Analysis
 
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Space and time are inseparable, and integrating the temporal aspect of your data into your spatial analysis leads to powerful discoveries. This workshop will build on the cluster analysis methods discussed in Spatial Data Mining I by presenting advanced techniques for analyzing your data in the context of both space and time. We will cover space-time pattern mining techniques including aggregating your temporal data into a space-time cube, emerging hot spot analysis, local outlier analysis, best practices for visualizing your space-time cube, and strategies for interpreting and sharing your results. Come learn how to use these new techniques to get the most out of your spatiotemporal data.
Views: 9190 Esri Events
More Data Mining with Weka (2.5: Evaluating 2‐class classification)
 
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More Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 5: Evaluating 2‐class classification http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/QldvyV https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 7323 WekaMOOC
Data Mining: Mastering Data Mining Skills | Part - 2
 
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In this video, Qasim Ali Shah talking on the topic "DATA MINING SKILLS". In this session you will know about the content of trainers. He is giving some useful tips to all students, like: how should you can select your topic to speak effectively and after this what type of content will be helpful for your topic. You will know so many more after watching this video regarding above given topic. ===== ABOUT Qasim Ali Shah ===== Qasim Ali Shah is a Public Speaker- Teacher- Writer- Corporate Trainer & Leader for every age group- Businessmen- Corporate executives- Employees- Students- Housewives- Networkers- Sportsmen and for all who wish everlasting Success- Happiness- Peace and Personal Growth. He helps people to change their belief & thought pattern- experience less stress and more success in their lives through better communication- positive thinking and spiritual knowledge. ===== FOLLOW ME ON THE SOCIALS ===== - Qasim Ali Shah: https://goo.gl/6BKcxu - Google+: https://goo.gl/uPyGvT - Twitter: https://goo.gl/78MVoA - Website : https://goo.gl/Tgjy6u ===== Team Member: Waqas Nasir =====
Views: 8203 Qasim Ali Shah
Lecture - 35 Data Mining and Knowledge Discovery Part II
 
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Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 43357 nptelhrd
DATA MINING   1 Data Visualization   3 2 1  Principal Component Analysis
 
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https://www.coursera.org/learn/datavisualization
Views: 626 Ryo Eng
Data Mining with Weka (2.2: Training and testing)
 
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Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 2: Training and testing http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/D3ZVf8 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 74493 WekaMOOC
DATA MINING   1 Data Visualization   3 2 2  Multidimensional Scaling
 
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https://www.coursera.org/learn/datavisualization
Views: 10363 Ryo Eng
Data Mining with Weka (2.1: Be a classifier!)
 
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Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 1: Be a classifier! http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/D3ZVf8 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 55595 WekaMOOC
Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience 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 For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Website: https://www.edureka.co/data-science 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. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 70298 edureka!
Data Mining with Weka (2.4: Baseline accuracy)
 
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Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 4: Baseline accuracy http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/D3ZVf8 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 36011 WekaMOOC
DATA MINING   1 Data Visualization   2 2 2  Parallel Coordinates
 
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https://www.coursera.org/learn/datavisualization
Views: 1203 Ryo Eng
Statistical Aspects of Data Mining (Stats 202) Day 2
 
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Google Tech Talks June 29, 2007 ABSTRACT This is the Google campus version of Stats 202 which is being taught at Stanford this summer. I will follow the material from the Stanford class very closely. That material can be found at www.stats202.com. The main topics are exploring and visualizing data, association analysis, classification, and clustering. The textbook is Introduction to Data Mining by Tan, Steinbach and Kumar. Googlers are welcome to attend any classes which they think might be of interest to them. Credits: Speaker:David Mease
Views: 60779 GoogleTechTalks
YouTube Data API - Data Mining #2
 
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Data mining YouTube using youtube.search.list and youtube.videos.list to forecast the senate races of 2014. And quantifying our probability using 2012 senate races data and stats from YouTube during the same period. Github/NBViewer Link: http://nbviewer.ipython.org/github/twistedhardware/mltutorial/blob/master/notebooks/data-mining/2.%20YouTube%20Data.ipynb
Views: 6941 Roshan
Data Mining with Weka (2.6: Cross-validation results)
 
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Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 6: Cross-validation results http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/D3ZVf8 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 29758 WekaMOOC
Introduction to Data Mining  (2/3)
 
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http://www.creativecommit.com. This video gives a brief demo of the various data mining techniques. The demo mainly uses SQL server 2008, BIDS 2008 and Excel for data mining
Views: 26922 creativecommIT
Data Mining with Weka (2.3: Repeated training and testing)
 
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Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 3: Repeated training and testing http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/D3ZVf8 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 45556 WekaMOOC
Introduction to data mining and architecture  in hindi
 
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#datamining #datawarehouse #lastmomenttuitions 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://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [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: 213287 Last moment tuitions
Introduction to Data Mining: Data Attributes (Part 2)
 
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In this Data Mining Fundamentals video tutorial, we dive even deeper into attributes by identifying the subsets of attribute classification. These subsets include: categorical, nominal, ordinal, interval and ratio. -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8Ljk0 See what our past attendees are saying here: https://hubs.ly/H0f8L-10 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 9325 Data Science Dojo
Data Warehouse & Mining 2 Data Warehouse Features |lecture| tutorial|sanjaypathakjec
 
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data warehouse tutorial and lecture of data warehouse features Data warehouse have mainly four features 1 subject oriented 2 integrated 3 time variant 4 non volatile
Views: 10246 Sanjay Pathak
Introduction to Data Mining: Similarity & Dissimilarity
 
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In this Data Mining Fundamentals tutorial, we introduce you to similarity and dissimilarity. Similarity is a numerical measure of how alike two data objects are, and dissimilarity is a numerical measure of how different two data objects are. We also discuss similarity and dissimilarity for single attributes. -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8Lsn0 See what our past attendees are saying here: https://hubs.ly/H0f8Lsp0 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 18000 Data Science Dojo
Time Series data Mining Using the Matrix Profile part 2
 
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Time Series data Mining Using the Matrix Profile: A Unifying View of Motif Discovery, Anomaly Detection, Segmentation, Classification, Clustering and Similarity Joins Part 2 Authors: Abdullah Al Mueen, Department of Computer Science, University of New Mexico Eamonn Keogh, Department of Computer Science and Engineering, University of California, Riverside Abstract: The Matrix Profile (and the algorithms to compute it: STAMP, STAMPI, STOMP, SCRIMP and GPU-STOMP), has the potential to revolutionize time series data mining because of its generality, versatility, simplicity and scalability. In particular it has implications for time series motif discovery, time series joins, shapelet discovery (classification), density estimation, semantic segmentation, visualization, clustering etc. Link to tutorial: http://www.cs.ucr.edu/~eamonn/MatrixProfile.html More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 1017 KDD2017 video
Rare Creatures and Lost Assets - Data Mining Gw2
 
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First of all a big thanks to that_shaman on reddit who found all this stuff! Links: https://www.guildwars2.com/en-gb/the-game/releases/october-15-2013/ Chris and the feedback initiative: https://forum-en.guildwars2.com/forum/game/gw2/Collaborative-Development/page/21#post3021865 The reddit thread: http://www.reddit.com/r/Guildwars2/comments/1o3a8n/data_mining_removed_unused_and_forgotten_models/
Views: 42469 WoodenPotatoes
Online surveillance software / data mining
 
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A look at how monitoring patterns of behavior online can be construed as subversive behavior. Will this become the truncheon of a world police state?
Views: 37654 germanjournal
Introduction to Datawarehouse in hindi | Data warehouse and data mining Lectures
 
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#datawarehouse #datamining #lastmomenttuitions 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://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [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: 285496 Last moment tuitions
Data mining tutorial for beginners FREE Training 01
 
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Published on Aug 2, 2014 1 intro data mining and scraping next tutorial here: http://youtu.be/gb4ufqFkT7A please comment below if you have any questions. Tq Category Education License Standard YouTube License
Views: 112736 Red Team Cyber Security
Mining Twitter with Python : 2 - Collecting data from Twitter
 
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In order to interact with the Twitter APIs, we need a Python client that implements the different calls to the APIs itself. We will use Tweepy in these tutorials and see how to build our application in multiple parts to collects data from our own Twitter timeline and other users timeline. ----- ------ Channel link: https://goo.gl/nVWDos Subscribe here: https://goo.gl/gMdGUE Link to playlist: https://goo.gl/WIHiEy ---- Join my Facebook Group to stay connected: http://bit.ly/2lZ3FC5 Like my Facebbok Page for updates: https://www.facebook.com/tigerstylecodeacademy/ Follow me on Twitter: https://twitter.com/sukhsingh Profile on LinkedIn: https://www.linkedin.com/in/singhsukh/ ---- Schedule: New educational videos every week ----- ----- Source Code for tutorials on Youtube: http://bit.ly/2nSQSAT ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh
Views: 9062 Sukhvinder Singh
Amazon, Google Data Mining Is Bad for Consumers, Betaworks CEO Says
 
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Feb.12 -- Google and Amazon are demanding a continuous stream of customer information from smart-home manufacturers, prompting privacy concerns. Bloomberg's Matt Day and John Borthwick, chief executive officer of Betaworks, discuss on "Bloomberg Technology."
Views: 1772 Bloomberg Technology
Frequent Pattern (FP) growth Algorithm for Association Rule Mining
 
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The FP-Growth Algorithm, proposed by Han, is an efficient and scalable method for mining the complete set of frequent patterns by pattern fragment growth, using an extended prefix-tree structure for storing compressed and crucial information about frequent patterns named frequent-pattern tree (FP-tree).
Views: 99767 StudyKorner
Data Mining with Weka (1.5: Using a filter )
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 5: Using a filter http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 68214 WekaMOOC
K-Mean Clustering
 
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Data Warehouse and Mining For more: http://www.anuradhabhatia.com
Views: 113070 Anuradha Bhatia
Data Mining with Weka (1.6: Visualizing your data)
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 6: Visualizing your data http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 68686 WekaMOOC
Data Mining Lecture 1 Part 2
 
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Introduction
Views: 738 Utah Data
Introduction to Data Mining: Euclidean Distance & Cosine Similarity
 
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In this Data Mining Fundamentals tutorial, we continue our introduction to similarity and dissimilarity by discussing euclidean distance and cosine similarity. We will show you how to calculate the euclidean distance and construct a distance matrix. -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8M8m0 See what our past attendees are saying here: https://hubs.ly/H0f8Lts0 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 22505 Data Science Dojo
Naive Bayes Classifier Algorithm Example Data Mining | Bayesian Classification | Machine Learning
 
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naive Bayes classifiers in data mining or machine learning are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features. Naive Bayes has been studied extensively since the 1950s. It was introduced under a different name into the text retrieval community in the early 1960s,and remains a popular (baseline) method for text categorization, the problem of judging documents as belonging to one category or the other (such as spam or legitimate, sports or politics, etc.) with word frequencies as the features. With appropriate pre-processing, it is competitive in this domain with more advanced methods including support vector machines. It also finds application in automatic medical diagnosis. for more refer to https://en.wikipedia.org/wiki/Naive_Bayes_classifier naive bayes classifier example for play-tennis Download PDF of the sum on below link https://britsol.blogspot.in/2017/11/naive-bayes-classifier-example-pdf.html *****************************************************NOTE********************************************************************************* The steps explained in this video is correct but please don't refer the given sum from the book mentioned in this video coz the solution for this problem might be wrong due to printing mistake. **************************************************************************************************************************************** All data mining algorithm videos Data mining algorithms Playlist: http://www.youtube.com/playlist?list=PLNmFIlsXKJMmekmO4Gh6ZBZUVZp24ltEr ******************************************************************** book name: techmax publications datawarehousing and mining by arti deshpande n pallavi halarnkar *********************************************
Views: 41941 fun 2 code
Data Mining the Deceased Trailer. Brandeis University, Lown 002. Nov 2 2:00-3:30
 
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Trailer for Data Mining the Deceased: Ancestry and the Business of Family. Brandeis University Lown OO2. Nov 2. 2:00-3:30 with Q and A
Views: 1689 Julia Creet
Data Warehouse and Data Mining AKTU/UPTU    Unit 1 - Part 2
 
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In this video I am teaching Data Warehouse and Data Mining - Btech AKTU/UPTU syllabus. this is the part one of unit 1. I have coverd topic like building a data warehouse , mapping data warehouse to architecture , data warehouse architecture for parallel processing etc. Please subscribe the channel , like and share the video with friends. Thank you. Boss Education link of all videos unit 1 Part 1 https://www.youtube.com/watch?v=T7G3cuzWXT8&list=PLy46393w6xPuChsqyCztxF4LZa9lXYuof&index=2&t=4s unit 1 Part 2 https://www.youtube.com/watch?v=Ffi0LE7Ofkk&list=PLy46393w6xPuChsqyCztxF4LZa9lXYuof&index=2 unit 1 Part 3 https://www.youtube.com/watch?v=rrJ1daXtbPo&list=PLy46393w6xPuChsqyCztxF4LZa9lXYuof&index=4&t=2s unit 2 Part 1 https://www.youtube.com/watch?v=gPAeXcnDmOE&list=PLy46393w6xPuChsqyCztxF4LZa9lXYuof&index=4 unit 2 Part 2 https://www.youtube.com/watch?v=3R2gQwuriqI&list=PLy46393w6xPuChsqyCztxF4LZa9lXYuof&index=5 unit 2 Part 3 https://www.youtube.com/watch?v=4iZ4vBRImqo&list=PLy46393w6xPuChsqyCztxF4LZa9lXYuof&index=6
Views: 104 BOSS EDUCATION
apriori algorithm (data mining)
 
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********************************************* visit below link for examples http://britsol.blogspot.in/2017/08/apriori-algorithm-example.html book name : techmax publications datawarehousing and mining by arti deshpande n pallavi halarnkar ******************************************** MORE DATA MINING ALGORITHM PLAYLIST IS ON BELOW LINK: https://www.youtube.com/watch?v=JZepOmvB514&list=PLNmFIlsXKJMmekmO4Gh6ZBZUVZp24ltEr
Views: 94057 fun 2 code
Le Data Mining en 35 Leçons - Session 2 : La méthodologie CRISP
 
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Cette seconde session du « Data Mining en 35 Leçons avec STATISTICA » décrit la méthodologie CRISP-DM (Cross Industry Standard Process pour le Data Mining) et sa mise en œuvre dans STATISTICA. Le CRISP-DM décompose le processus du data mining en 6 étapes majeures : La compréhension de la problématique, la compréhension des données, la préparation des données, la phase de modélisation, l'évaluation et enfin, le déploiement
Views: 11758 Statistica France