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Data Warehousing and Data Mining
 
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This course aims to introduce advanced database concepts such as data warehousing, data mining techniques, clustering, classifications and its real time applications. SlideTalk video created by SlideTalk at http://slidetalk.net, the online solution to convert powerpoint to video with automatic voice over.
Views: 5876 SlideTalk
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: 262660 Last moment tuitions
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: 331543 Last moment tuitions
1 - Introduction to Data warehouse and Data warehousing
 
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Short Introduction Video to understand, What is Data warehouse and Data warehousing? How it is different from Database? It also talks about properties of Data warehouse which are Subject Oriented, Integrated, Time Variant, Non Volatile ETL Tools: Talend Open Studio, Jaspersoft ETL, Ab initio, Informatica, Datastage, Clover ETL, Pentaho ETL, Kettle. #datawarehouse #ETL #DWH Business Intelligence tools: Oracle BI, Microsoft BI suite, Tableau, Qlik, Jaspersoft BI, Pentabo BI, Miscrostrategy, Tibco For more details visit: http://www.vikramtakkar.com/2015/08/what-is-datawarehouse-and.html Datawarehouse Playlist: https://www.youtube.com/playlist?list=PLJ4bGndMaa8FV7nrvKXeHCLRMmIXVCyOG
Views: 120769 Vikram Takkar
Last Minute Tutorials | Data mining | Introduction | Examples
 
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Please feel free to get in touch with me :) If it helped you, please like my facebook page and don't forget to subscribe to Last Minute Tutorials. Thaaank Youuu. Facebook: https://www.facebook.com/Last-Minute-Tutorials-862868223868621/ Website: www.lmtutorials.com For any queries or suggestions, kindly mail at: [email protected]
Views: 55022 Last Minute Tutorials
Data Mining
 
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Technology students give presentation on about Data Mining including the advantages/disadvantages, how to and more.
Views: 17141 techEIU
Data Mining Classification and Prediction ( in Hindi)
 
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A tutorial about classification and prediction in Data Mining .
Views: 40805 Red Apple Tutorials
Data Warehouse Tutorial For Beginners | Data Warehouse Concepts | Data Warehousing | Edureka
 
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** Data Warehousing & BI Training: https://www.edureka.co/data-warehousing-and-bi ** This Data Warehouse Tutorial For Beginners will give you an introduction to data warehousing and business intelligence. You will be able to understand basic data warehouse concepts with examples. The following topics have been covered in this tutorial: 1. What Is The Need For BI? 2. What Is Data Warehousing? 3. Key Terminologies Related To DWH Architecture: a. OLTP Vs OLAP b. ETL c. Data Mart d. Metadata 4. DWH Architecture 5. Demo: Creating A DWH - - - - - - - - - - - - - - Check our complete Data Warehousing & Business Intelligence playlist here: https://goo.gl/DZEuZt. #DataWarehousing #DataWarehouseTutorial #DataWarehouseTraining Subscribe to our channel to get video updates. Hit the subscribe button above. - - - - - - - - - - - - - - How it Works? 1. This is a 5 Week Instructor led Online Course, 25 hours of assignment and 10 hours of project work 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. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course: Edureka's Data Warehousing and Business Intelligence Course, will introduce participants to create and work with leading ETL & BI tools like: 1. Talend 5.x to create, execute, monitor and schedule ETL processes. It will cover concepts around Data Replication, Migration and Integration Operations 2. Tableau 9.x for data visualization to see how easy and reliable data visualization can become for representation with dashboards 3. Data Modeling tool ERwin r9 to create a Data Warehouse or Data Mart - - - - - - - - - - - - - - Who should go for this course? The following professionals can go for this course: 1. Data warehousing enthusiasts 2. Analytics Managers 3. Data Modelers 4. ETL Developers and BI Developers - - - - - - - - - - - - - - Why learn Data Warehousing and Business Intelligence? All the successful companies have been investing large sums of money in business intelligence and data warehousing tools and technologies. Up-to-date, accurate and integrated information about their supply chain, products and customers are critical for their success. With the advent of Mobile, Social and Cloud platform, today's business intelligence tools have evolved and can be categorized into five areas, including databases, extraction transformation and load (ETL) tools, data quality tools, reporting tools and statistical analysis tools. This course will provide a strong foundation around Data Warehousing and Business Intelligence fundamentals and sophisticated tools like Talend, Tableau and ERwin. - - - - - - - - - - - - - - For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka - - - - - - - - - - - - - - Customer Review: Kanishk says, "Underwent Mastering in DW-BI Course. The training material and trainer are up to the mark to get yourself acquainted to the new technology. Very helpful support service from Edureka."
Views: 268589 edureka!
Data Mining PPT
 
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Views: 182 Gurdev Salhotra
Data Warehousing: Final Presentation
 
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By Matt Fonagy
Views: 20 Matthew Fonagy
3 tier architecture of Data warehouse
 
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Arshdeep Kaur ( Department of Computer Applications )
Views: 8042 Techbytes ACET 2018
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: 238563 Thales Sehn Körting
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.
Data Warehouse
 
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This video is about Data Warehouse. If you want this ppt the link is given below: click here for ppt: - https://goo.gl/HreJ55 if you want another ppt on different then comment Thanks for watching
Views: 33 Champ Talk
Data Warehouse Concepts | Data Warehouse Tutorial | Data Warehouse Architecture | Edureka
 
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***** Data Warehousing & BI Training: https://www.edureka.co/data-warehousing-and-bi ***** This tutorial on data warehouse concepts will tell you everything you need to know in performing data warehousing and business intelligence. The various data warehouse concepts explained in this video are: 1. What Is Data Warehousing? 2. Data Warehousing Concepts: 3. OLAP (On-Line Analytical Processing) 4. Types Of OLAP Cubes 5. Dimensions, Facts & Measures 6. Data Warehouse Schema - - - - - - - - - - - - - - Check our complete Data Warehousing & Business Inelligence playlist here: https://goo.gl/DZEuZt. #DataWarehousing #DataWarehouseTutorial #DataWarehouseTraining #DataWarehouseConcepts Subscribe to our channel to get video updates. Hit the subscribe button above. - - - - - - - - - - - - - - How it Works? 1. This is a 5 Week Instructor led Online Course, 25 hours of assignment and 10 hours of project work 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. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course: Edureka's Data Warehousing and Business Intelligence Course, will introduce participants to create and work with leading ETL & BI tools like: 1. Talend 5.x to create, execute, monitor and schedule ETL processes. It will cover concepts around Data Replication, Migration and Integration Operations 2. Tableau 9.x for data visualization to see how easy and reliable data visualization can become for representation with dashboards 3. Data Modeling tool ERwin r9 to create a Data Warehouse or Data Mart - - - - - - - - - - - - - - Who should go for this course? The following professionals can go for this course: 1. Data warehousing enthusiasts 2. Analytics Managers 3. Data Modelers 4. ETL Developers and BI Developers - - - - - - - - - - - - - - Why learn Data Warehousing and Business Intelligence? All the successful companies have been investing large sums of money in business intelligence and data warehousing tools and technologies. Up-to-date, accurate and integrated information about their supply chain, products and customers are critical for their success. With the advent of Mobile, Social and Cloud platform, today's business intelligence tools have evolved and can be categorized into five areas, including databases, extraction transformation and load (ETL) tools, data quality tools, reporting tools and statistical analysis tools. This course will provide a strong foundation around Data Warehousing and Business Intelligence fundamentals and sophisticated tools like Talend, Tableau and ERwin. - - - - - - - - - - - - - - For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka - - - - - - - - - - - - - - Customer Review: Kanishk says, "Underwent Mastering in DW-BI Course. The training material and trainer are up to the mark to get yourself acquainted to the new technology. Very helpful support service from Edureka."
Views: 58718 edureka!
Web Mining - Tutorial
 
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Web Mining Web Mining is the use of Data mining techniques to automatically discover and extract information from World Wide Web. There are 3 areas of web Mining Web content Mining. Web usage Mining Web structure Mining. Web content Mining Web content Mining is the process of extracting useful information from content of web document.it may consists of text images,audio,video or structured record such as list & tables. screen scaper,Mozenda,Automation Anywhere,Web content Extractor, Web info extractor are the tools used to extract essential information that one needs. Web Usage Mining Web usage Mining is the process of identifying browsing patterns by analysing the users Navigational behaviour. Techniques for discovery & pattern analysis are two types. They are Pattern Analysis Tool. Pattern Discovery Tool. Data pre processing,Path Analysis,Grouping,filtering,Statistical Analysis, Association Rules,Clustering,Sequential Pattterns,classification are the Analysis done to analyse the patterns. Web structure Mining Web structure Mining is a tool, used to extract patterns from hyperlinks in the web. Web structure Mining is also called link Mining. HITS & PAGE RANK Algorithm are the Popular Web structure Mining Algorithm. By applying Web content mining,web structure Mining & Web usage Mining knowledge is extracted from web data.
DATA WAREHOUSING Basics
 
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The first video in the series on data warehousing.
Views: 157496 eDewcate
Data Mining  Association Rule - Basic Concepts
 
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short introduction on Association Rule with definition & Example, are explained. Association rules are if/then statements used to find relationship between unrelated data in information repository or relational database. Parts of Association rule is explained with 2 measurements support and confidence. types of association rule such as single dimensional Association Rule,Multi dimensional Association rules and Hybrid Association rules are explained with Examples. Names of Association rule algorithm and fields where association rule is used is also mentioned.
Database VS Data Warehouse
 
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Whats the difference between a Database and a Data Warehouse? I had a attendee ask this question at one of our workshops. In this short video I explain the distinction. Here's the link mentioned in the video:https://intricity.attach.io/r1x~TiWdz Talk with an Intricity Specialist: https://www.intricity.com/intricity101/ www.intricity.com
Views: 12100 Intricity101
What is Business Intelligence (BI)?
 
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There are many definitions for Business Intelligence, or BI. To put it simply, BI is about delivering relevant and reliable information to the right people at the right time with the goal of achieving better decisions faster. If you wanna have efficient access to accurate, understandable and actionable information on demand, then BI might be right for your organization. For more information, contact Hitachi Solutions Canada (canada.hitachi-solutions.com).
Views: 399828 Hitachi Solutions Canada
Data Warehouse tutorial. Creating an ETL.
 
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All Data Warehouse tutorial: https://www.youtube.com/playlist?list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt Lesson 1: Date Warehouse Tutorial - Introduction https://www.youtube.com/watch?v=AfIHINaKD9M&list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt&index=2&t=5s Lesson 2: Data Warehouse Tutorial - Creating database https://www.youtube.com/watch?v=b_0RrFXnlhc&list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt&index=3&t=511s Lesson 3: Data Warehouse Tutorial - Creating an OLAP cube - Data Warehouse for beginners https://www.youtube.com/watch?v=Z00VTv0GA9I&list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt&index=4&t=0s Lesson 4: Data Warehouse tutorial. Creating an ETL https://www.youtube.com/watch?v=9Akvz2x0az4&list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt&index=5&t=1371s Lesson 5: Data Warehouse Tutorial - Jobs - Data Warehouse for beginners https://www.youtube.com/watch?v=b997bACm_mE&list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt&index=6&t=13s Lesson 6: Data Warehouse Tutorial - Pivot Table in Excel and data presentation https://www.youtube.com/watch?v=n7K6JEvcYFg&list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt&index=7&t=3s This Data Warehouse video tutorial demonstrates how to create ETL (Extract, Load, Transform) package.
Data Mining & Business Intelligence | Tutorial #30 | BI Architecture
 
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Order my books at 👉 http://www.tek97.com/ #RanjiRaj #BusinessIntelligence #Architecture Follow me on Instagram 👉 https://www.instagram.com/reng_army/ Visit my Profile 👉 https://www.linkedin.com/in/reng99/ Support my work on Patreon 👉 https://www.patreon.com/ranjiraj Follow me on Instagram 👉 https://www.instagram.com/reng_army/ Visit my Profile 👉 https://www.linkedin.com/in/reng99/ Support my work on Patreon 👉 https://www.patreon.com/ranjiraj So here, basically if you look at architectures that I have presented in the slides, so this is a most simplified architecture of BI and since we need to look at in a holistic perspective so I tried to create 3 layers one is a data sources, the bottom layer is the data sources, and the middle layer is talking about the data warehouse and the top layer is the Business Intelligence. إذاً هنا ، في الأساس ، إذا نظرت إلى البِنى التي قمت بعرضها في الشرائح ، لذا فهذه هي البنية الأكثر بساطة من BI وحيث أننا نحتاج إلى النظر في منظور كلي لذا حاولت إنشاء 3 طبقات واحد هو مصدر بيانات ، الطبقة السفلية هي مصادر البيانات ، والطبقة الوسطى تتحدث عن مستودع البيانات والطبقة العليا هي ذكاء الأعمال. Así que aquí, básicamente, si miras las arquitecturas que he presentado en las diapositivas, esta es la arquitectura más simplificada de BI y, como tenemos que ver desde una perspectiva holística, intenté crear 3 capas, una fuente de datos, la capa inferior son las fuentes de datos, y la capa intermedia habla sobre el almacén de datos y la capa superior es la inteligencia empresarial. Also hier, im Grunde genommen, wenn man sich Architekturen anschaut, die ich in den Folien vorgestellt habe, ist dies eine sehr vereinfachte Architektur von BI und da wir uns in einer ganzheitlichen Perspektive betrachten müssen, habe ich versucht, 3 Ebenen zu erstellen. Die unterste Schicht ist die Datenquelle, die mittlere Schicht spricht über das Data Warehouse und die oberste Schicht ist die Business Intelligence. Итак, здесь, в основном, если вы посмотрите на архитектуры, которые я представил на слайдах, так что это самая упрощенная архитектура BI, и поскольку нам нужно взглянуть на целостную перспективу, поэтому я попытался создать 3 слоя, один из них - источники данных, нижний уровень - это источники данных, а средний уровень - это хранилище данных, а верхний уровень - это бизнес-аналитика. Donc ici, fondamentalement, si vous regardez les architectures que j'ai présentées dans les diapositives, donc c'est une architecture de BI simplifiée et comme nous devons regarder dans une perspective holistique, j'ai essayé de créer 3 couches, une source de données, la couche inférieure correspond aux sources de données et la couche intermédiaire à l'entrepôt de données et la couche supérieure à la Business Intelligence. ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ 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: 5029 Ranji Raj
Lecture - 30 Introduction to Data Warehousing and OLAP
 
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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: 212643 nptelhrd
Data Mining Software in Healthcare
 
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This is a brief discussion of data mining software with an emphasis on the healthcare field.
Views: 4302 Joshua White
Data Preprocessing
 
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Project Name: Learning by Doing (LBD) based course content development Project Investigator: Prof Sandhya Kode
Views: 39935 Vidya-mitra
Data Warehouse Tutorial - Creating an OLAP cube - Data Warehouse for beginners (Lesson 3)
 
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All #Data Warehouse tutorial: https://www.youtube.com/playlist?list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt Lesson 1: Date Warehouse Tutorial - Introduction https://www.youtube.com/watch?v=AfIHINaKD9M&list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt&index=2&t=5s Lesson 2: Data Warehouse Tutorial - Creating database https://www.youtube.com/watch?v=b_0RrFXnlhc&list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt&index=3&t=511s Lesson 3: Data Warehouse Tutorial - Creating an OLAP cube - Data Warehouse for beginners https://www.youtube.com/watch?v=Z00VTv0GA9I&list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt&index=4&t=0s Lesson 4: Data Warehouse tutorial. Creating an ETL https://www.youtube.com/watch?v=9Akvz2x0az4&list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt&index=5&t=1371s Lesson 5: Data Warehouse Tutorial - Jobs - Data Warehouse for beginners https://www.youtube.com/watch?v=b997bACm_mE&list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt&index=6&t=13s Lesson 6: Data Warehouse Tutorial - Pivot Table in Excel and data presentation https://www.youtube.com/watch?v=n7K6JEvcYFg&list=PL99-DcFspRUoWh6w2E1gI-SR54Oq3M2lt&index=7&t=3s Data Warehouse Tutorial for beginners: creating an OLAP cube
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: 208388 Well Academy
Data Mining & Business Intelligence | Tutorial #33 | Decision Support System
 
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Order my books at 👉 http://www.tek97.com/ #RanjiRaj #BusinessIntelligence #DSS Follow me on Instagram 👉 https://www.instagram.com/reng_army/ Visit my Profile 👉 https://www.linkedin.com/in/reng99/ Support my work on Patreon 👉 https://www.patreon.com/ranjiraj This video will demonstrate you regarding the Decision Support System DSS in Business Intelligence. Watch Now ! سيوضح لك هذا الفيديو فيما يتعلق بنظام دعم القرار DSS في ذكاء الأعمال. شاهد الآن ! Este video lo demostrará con respecto al sistema de soporte de decisión DSS en Business Intelligence. Ver ahora ! Dieses Video zeigt Ihnen das Entscheidungshilfesystem DSS in Business Intelligence. Schau jetzt ! Это видео продемонстрирует вам информацию о системе поддержки решений DSS в Business Intelligence. Смотри ! Cette vidéo vous présentera le système d'aide à la décision DSS en Business Intelligence. 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: 1596 Ranji Raj
Data Mining Lecture - - Finding frequent item sets | Apriori Algorithm | Solved Example (Eng-Hindi)
 
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In this video Apriori algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining in hindi, Finding frequent item sets, data mining, data mining algorithms in hindi, data mining lecture, data mining tools, data mining tutorial,
Views: 265897 Well Academy
Temporal Database in Hindi
 
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A temporal database is a database with built-in support for handling data involving time, being related to the slowly changing dimension concept, for example a temporal data model and a temporal version of Structured Query Language (SQL). More specifically the temporal aspects usually include valid time and transaction time. These attributes can be combined to form bitemporal data. Valid time is the time period during which a fact is true in the real world. Transaction time is the time period during which a fact stored in the database was known. Bitemporal data combines both Valid and Transaction Time. It is possible to have timelines other than Valid Time and Transaction Time, such as Decision Time, in the database. In that case the database is called a multitemporal database as opposed to a bitemporal database. However, this approach introduces additional complexities such as dealing with the validity of (foreign) keys. Temporal databases are in contrast to current databases (at term that doesn't mean, currently available databases, some do have temporal features, see also below), which store only facts which are believed to be true at the current time. Temporal databases supports System-maintained transaction time. With the development of SQL and its attendant use in real-life applications, database users realized that when they added date columns to key fields, some issues arose. For example, if a table has a primary key and some attributes, adding a date to the primary key to track historical changes can lead to creation of more rows than intended. Deletes must also be handled differently when rows are tracked in this way. In 1992, this issue was recognized but standard database theory was not yet up to resolving this issue, and neither was the then-newly formalized SQL-92 standard. Richard Snodgrass proposed in 1992 that temporal extensions to SQL be developed by the temporal database community. In response to this proposal, a committee was formed to design extensions to the 1992 edition of the SQL standard (ANSI X3.135.-1992 and ISO/IEC 9075:1992); those extensions, known as TSQL2, were developed during 1993 by this committee.[3] In late 1993, Snodgrass presented this work to the group responsible for the American National Standard for Database Language SQL, ANSI Technical Committee X3H2 (now known as NCITS H2). The preliminary language specification appeared in the March 1994 ACM SIGMOD Record. Based on responses to that specification, changes were made to the language, and the definitive version of the TSQL2 Language Specification was published in September, 1994[4] An attempt was made to incorporate parts of TSQL2 into the new SQL standard SQL:1999, called SQL3. Parts of TSQL2 were included in a new substandard of SQL3, ISO/IEC 9075-7, called SQL/Temporal.[3] The TSQL2 approach was heavily criticized by Chris Date and Hugh Darwen.[5] The ISO project responsible for temporal support was canceled near the end of 2001. As of December 2011, ISO/IEC 9075, Database Language SQL:2011 Part 2: SQL/Foundation included clauses in table definitions to define "application-time period tables" (valid time tables), "system-versioned tables" (transaction time tables) and "system-versioned application-time period tables" (bitemporal tables). A substantive difference between the TSQL2 proposal and what was adopted in SQL:2011 is that there are no hidden columns in the SQL:2011 treatment, nor does it have a new data type for intervals; instead two date or timestamp columns can be bound together using a PERIOD FOR declaration. Another difference is replacement of the controversial (prefix) statement modifiers from TSQL2 with a set of temporal predicates. For illustration, consider the following short biography of a fictional man, John Doe: John Doe was born on April 3, 1975 in the Kids Hospital of Medicine County, as son of Jack Doe and Jane Doe who lived in Smallville. Jack Doe proudly registered the birth of his first-born on April 4, 1975 at the Smallville City Hall. John grew up as a joyful boy, turned out to be a brilliant student and graduated with honors in 1993. After graduation he went to live on his own in Bigtown. Although he moved out on August 26, 1994, he forgot to register the change of address officially. It was only at the turn of the seasons that his mother reminded him that he had to register, which he did a few days later on December 27, 1994. Although John had a promising future, his story ends tragically. John Doe was accidentally hit by a truck on April 1, 2001. The coroner reported his date of death on the very same day.
Views: 14047 Introtuts
BIG Data Explained - Data all Around 😱 😱 😱
 
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Namaskaar Dosto, is video mein maine aapse BIG Data ke baare mein baat ki hai, Data ke baare mein toh aap sabhi jaante hai but BIG Data ka concept sabse alag hai. Yeh kya hai aur kaise kaam karta hai yeh maine aapko is video mein bataya hai. Mujhe umeed hai ki BIG data ke baare mein aapko yeh video pasand aayegi. Share, Support, Subscribe!!! Subscribe: http://bit.ly/1Wfsvt4 Android App: https://technicalguruji.in/app Youtube: http://www.youtube.com/c/TechnicalGuruji Twitter: http://www.twitter.com/technicalguruji Facebook: http://www.facebook.com/technicalguruji Facebook Myself: https://goo.gl/zUfbUU Instagram: http://instagram.com/technicalguruji Google Plus: https://plus.google.com/+TechnicalGuruji Website: https://technicalguruji.in/ Merchandise: http://shop.technicalguruji.in/ About : Technical Guruji is a YouTube Channel, where you will find technological videos in Hindi, New Video is Posted Everyday :)
Views: 191088 Technical Guruji
Decision Tree with Solved Example in English | DWM | ML | BDA
 
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Take the Full Course of Artificial Intelligence What we Provide 1) 28 Videos (Index is given down) 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in Artificial Intelligence Sample Notes : https://goo.gl/aZtqjh To buy the course click https://goo.gl/H5QdDU if you have any query related to buying the course feel free to email us : [email protected] Other free Courses Available : Python : https://goo.gl/2gftZ3 SQL : https://goo.gl/VXR5GX Arduino : https://goo.gl/fG5eqk Raspberry pie : https://goo.gl/1XMPxt Artificial Intelligence Index 1)Agent and Peas Description 2)Types of agent 3)Learning Agent 4)Breadth first search 5)Depth first search 6)Iterative depth first search 7)Hill climbing 8)Min max 9)Alpha beta pruning 10)A* sums 11)Genetic Algorithm 12)Genetic Algorithm MAXONE Example 13)Propsotional Logic 14)PL to CNF basics 15) First order logic solved Example 16)Resolution tree sum part 1 17)Resolution tree Sum part 2 18)Decision tree( ID3) 19)Expert system 20) WUMPUS World 21)Natural Language Processing 22) Bayesian belief Network toothache and Cavity sum 23) Supervised and Unsupervised Learning 24) Hill Climbing Algorithm 26) Heuristic Function (Block world + 8 puzzle ) 27) Partial Order Planing 28) GBFS Solved Example
Views: 296466 Last moment tuitions
KDD (Knowledge Discovery from Data | in HINDI | Data-Mining and Warehousing | CSVTU
 
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Knowledge discover- A very fundamental and simple topic. depicted complete process by diagram
Views: 807 Bunny funda
OPTICS : Ordering Points To Identify Clustering Algorithm Video | Clustering Analysis - ExcelR
 
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ExcelR: In this video, we will learn about the basic approach of OPTICS is similar to DBSCAN, but instead of maintaining a set of known, but so far unprocessed cluster members, a priority queue (e.g. using an indexed heap) is used. Things you will learn in this video 1)What is OPTICS? 2)What are drawbacks in DBSCAN? 3)Advantages & Disadvantages in OPTICS 4)What is OPTICS-Appendix? To buy eLearning course on Data Science click here https://goo.gl/oMiQMw To register for classroom training click here https://goo.gl/UyU2ve To Enroll for virtual online training click here https://goo.gl/JTkWXo SUBSCRIBE HERE for more updates: https://goo.gl/WKNNPx For K-Means Clustering Tutorial click here https://goo.gl/PYqXRJ For Introduction to Clustering click here Introduction to Clustering | Cluster Analysis #ExcelRSolutions #OPTICS#Differenttypesofclusterings#ClusterAnalytics#AdvantagesanddisadvantagesinOPTICS #DataSciencetutorial #DataScienceforbeginners #DataScienceTraining ----- For More Information: Toll Free (IND) : 1800 212 2120 | +91 80080 09706 Malaysia: 60 11 3799 1378 USA: 001-844-392-3571 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com Connect with us: Facebook: https://www.facebook.com/ExcelR/ LinkedIn: https://www.linkedin.com/company/exce... Twitter: https://twitter.com/ExcelrS G+: https://plus.google.com/+ExcelRSolutions
Decision Tree Induction (in Hindi)
 
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This Video is about Decision Tree Classification in Data Mining.
Views: 25772 Red Apple Tutorials
data mining fp growth | data mining fp growth algorithm | data mining fp tree example | fp growth
 
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In this video FP growth algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining algorithms in hindi, data mining in hindi, data mining lecture, data mining tools, data mining tutorial, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining fp growth, data mining fp growth algorithm, data mining fp tree example, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining, fp growth algorithm, fp growth algorithm example, fp growth algorithm in data mining, fp growth algorithm in data mining example, fp growth algorithm in data mining examples ppt, fp growth algorithm in data mining in hindi, fp growth algorithm in r, fp growth english, fp growth example, fp growth example in data mining, fp growth frequent itemset, fp growth in data mining, fp growth step by step, fp growth tree
Views: 165706 Well Academy
Data Mining   KDD Process
 
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KDD - knowledge discovery in Database. short introduction on Data cleaning,Data integration, Data selection,Data mining,pattern evaluation and knowledge representation.
DATA MINING CONCEPTS AND TECHNIQUES
 
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Data Mining
Views: 193 ARUN RAJ M
Designing a Data Warehouse from the Ground Up (periscope broadcast)
 
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Follow me on Twitter: http://www.twitter.com/sqldusty Join Dustin Ryan and Mitchell Pearson in a Pragmatic Works sponsored webinar as they present Designing a Data Warehouse from the Ground Up. View the full recording of this webinar, screen and all, right here: http://bit.ly/1E9KtVU Download the PowerPoint slides: http://bit.ly/1NiPNc5 Visit http://SQLDusty.com for more data warehouse design and Microsoft business intelligence tips and tricks.
Views: 5423 Dustin Ryan
Data Mining : Data Visualization Techniques
 
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This video explains various visualization techniques in data mining. Video Lecture by Anisha Lalwani.
Views: 4262 topNotch Tutorials
K Medoid with Sovled Example in Hindi | Clustering | Datawarehouse and Data mining series
 
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#kmedoid #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: 65594 Last moment tuitions
GIS Lecture 1 : What is Geospatial or Spatial Data and GIS (Geographical Information System)
 
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What is Geoaptial Data or Spatial Data or Geographic Information? What is GIS (Geographical Information System)
Views: 9635 Anuj Tiwari
Weka Data Mining Tutorial for First Time & Beginner Users
 
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23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 471738 Brandon Weinberg
Introduction To Artificial Neural Network Explained With Example In Hindi
 
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Views: 34717 5 Minutes Engineering
what is big data in hindi
 
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Hi Guys, this video is about to discuss what is big data nd hadoop, i hope you will like the video please click the like button and subscribe my channel. ----------------------------------------------------------------------------------------------------------- My ebook Real Ways to Make Money Online - E-book (Lifetime Free updates) (Rs.149) : https://goo.gl/oB95Pt Donate us to Keep Motivated paypal.me/techbulu Products I use Samson Go Mic: https://amzn.to/2LoefhP Pop filter: https://amzn.to/2uyZRJR Microsoft Office 365: https://amzn.to/2JBVP8y My phone: https://amzn.to/2uMuwCV Desktop : https://amzn.to/2JCe5yF Digital Pen: https://amzn.to/2LpvCin Share, Support, Subscribe!!! Youtube: https://www.youtube.com/c/TECHBULU Twitter: https://twitter.com/techbulu Facebook: https://www.facebook.com/techbulu/ Pinterest: https://www.pinterest.com/techbulu/ Google Plus: https://goo.gl/sZhdc0 Linkedin: https://www.linkedin.com/in/tech-bulu-15834b140/ BlogSite: http://www.techbulu.com/ About Us: TECHBULU is a YouTube Channel, where you will find technical and education videos. -----------------------------------------------------------------------------------------------------------
Views: 38401 TECH BULU