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Data Mining-Structured Data, Unstructured data and Information Retrieval
 
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Structured Data, Unstructured data and Information Retrieval
Views: 1251 John Paul
What is INFORMATION RETRIEVAL? What does INFORMATION RETRIEVAL mean? INFORMATION RETRIEVAL meaning
 
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What is INFORMATION RETRIEVAL? What does INFORMATION RETRIEVAL mean? INFORMATION RETRIEVAL meaning - INFORMATION RETRIEVAL definition - INFORMATION RETRIEVAL explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Information retrieval (IR) is the activity of obtaining information resources relevant to an information need from a collection of information resources. Searches can be based on full-text or other content-based indexing. Automated information retrieval systems are used to reduce what has been called "information overload". Many universities and public libraries use IR systems to provide access to books, journals and other documents. Web search engines are the most visible IR applications. An information retrieval process begins when a user enters a query into the system. Queries are formal statements of information needs, for example search strings in web search engines. In information retrieval a query does not uniquely identify a single object in the collection. Instead, several objects may match the query, perhaps with different degrees of relevancy. An object is an entity that is represented by information in a content collection or database. User queries are matched against the database information. However, as opposed to classical SQL queries of a database, in information retrieval the results returned may or may not match the query, so results are typically ranked. This ranking of results is a key difference of information retrieval searching compared to database searching. Depending on the application the data objects may be, for example, text documents, images, audio, mind maps or videos. Often the documents themselves are not kept or stored directly in the IR system, but are instead represented in the system by document surrogates or metadata. Most IR systems compute a numeric score on how well each object in the database matches the query, and rank the objects according to this value. The top ranking objects are then shown to the user. The process may then be iterated if the user wishes to refine the query.
Views: 8709 The Audiopedia
INFORMATION RETRIEVAL TECHNIQUES IN HINDI
 
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Find the notes of INFORMATION RETRIEVAL on this link - https://viden.io/knowledge/information-retrieval?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=ajaze-khan-1
Views: 5623 LearnEveryone
DATA MINING   2 Text Retrieval and Search Engines   1 1 2 Course Introduction Video
 
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https://www.coursera.org/learn/text-retrieval
Views: 544 Ryo Eng
DATA MINING   2 Text Retrieval and Search Engines   1 1 1 Course Welcome Video
 
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https://www.coursera.org/learn/text-retrieval
Views: 589 Ryo Eng
DATA MINING   2 Text Retrieval and Search Engines   Lesson 4 3 Query Likelihood Retrieval Function
 
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https://www.coursera.org/learn/text-retrieval
Views: 149 Ryo Eng
DATA MINING   2 Text Retrieval and Search Engines   Lesson 3 1 Evaluation of TR Systems
 
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https://www.coursera.org/learn/text-retrieval
Views: 83 Ryo Eng
Last Minute Tutorials | Data mining | Introduction | Examples
 
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NOTES:- Theory of computation : https://viden.io/knowledge/theory-of-computation?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 DAA(all topics are included in this link) : https://viden.io/knowledge/design-and-analysis-of-algorithms-topic-wise-ada?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 Advanced DBMS : https://viden.io/knowledge/advanced-dbms?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 for QM method-https://viden.io/knowledge/quine-mccluskey-method-qm-method?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 K-MAPS : https://viden.io/knowledge/k-maps-karnaugh-map?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 Basics of logic gates : https://viden.io/knowledge/basics-of-logic-gates-and-more?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 Website: https://lmtutorials.com/ Facebook: https://www.facebook.com/Last-Minute-Tutorials-862868223868621/ For any queries or suggestions, kindly mail at: [email protected]
Views: 27415 Last Minute Tutorials
DATA MINING   2 Text Retrieval and Search Engines   Lesson 4 1 Probabilistic Retrieval Model   Basic
 
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https://www.coursera.org/learn/text-retrieval
Views: 984 Ryo Eng
Evaluation 6: precision and recall
 
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Precision and recall are the two fundamental measures of search effectiveness. We discuss their building blocks (true/false positives/negatives), give a probabilistic interpretation, and provide an intuitive explanation of what they reflect. We also discuss why you should never report just the recall, or just the precision of a system.
Views: 16910 Victor Lavrenko
Lecture 17 — The Vector Space Model - Natural Language Processing | Michigan
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
DATA MINING   2 Text Retrieval and Search Engines   Lesson 4 2 Statistical Language Model
 
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https://www.coursera.org/learn/text-retrieval
Views: 184 Ryo Eng
DATA MINING   2 Text Retrieval and Search Engines   Lesson 4 5 Statistical Language Model Part 2
 
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https://www.coursera.org/learn/text-retrieval
Views: 64 Ryo Eng
DATA MINING   2 Text Retrieval and Search Engines   Lesson 2 4 Implementation of TR Systems
 
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https://www.coursera.org/learn/text-retrieval
Views: 109 Ryo Eng
DATA MINING   2 Text Retrieval and Search Engines   Lesson 5 2 Feedback in Vector Space Model   Rocc
 
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https://www.coursera.org/learn/text-retrieval
Views: 84 Ryo Eng
Hubs & Authorities
 
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Big Data Analytics For more: http://www.anuradhabhatia.com
Views: 19698 Anuradha Bhatia
DATA MINING   2 Text Retrieval and Search Engines   Lesson 5 5 Web Indexing
 
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https://www.coursera.org/learn/text-retrieval
Views: 89 Ryo Eng
WDM 2: Structured Data, Unstructured data and Information Retrieval
 
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What is an IR System For Full Course Experience Please Go To http://mentorsnet.org/course_preview?course_id=1 Full Course Experience Includes 1. Access to course videos and exercises 2. View & manage your progress/pace 3. In-class projects and code reviews 4. Personal guidance from your Mentors
Views: 11213 Oresoft LWC
PageRank Algorithm - Example
 
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Full Numerical Methods Course: https://bit.ly/2wYb2xf
Views: 40133 Balazs Holczer
DATA MINING   2 Text Retrieval and Search Engines   Lesson 2 3 Doc Length Normalization
 
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https://www.coursera.org/learn/text-retrieval
Views: 92 Ryo Eng
Lecture 59 — Hierarchical Clustering | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
INFORMATION RETRIEVAL TECHNIQUES
 
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Find the notes of INFORMATION RETRIEVAL on this link - https://viden.io/knowledge/information-retrieval?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=ajaze-khan-1
Views: 2825 LearnEveryone
DATA MINING   2 Text Retrieval and Search Engines   Lesson 6 3 Learning to Rank Part 3
 
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https://www.coursera.org/learn/text-retrieval
Views: 14 Ryo Eng
DATA MINING   2 Text Retrieval and Search Engines   Lesson 3 6 Evaluation of TR Systems   Practical
 
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https://www.coursera.org/learn/text-retrieval
Views: 32 Ryo Eng
DATA MINING   2 Text Retrieval and Search Engines   Lesson 3 3 Evaluation of TR Systems   Evaluating
 
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https://www.coursera.org/learn/text-retrieval
Views: 49 Ryo Eng
DATA MINING   2 Text Retrieval and Search Engines   Lesson 3 5 Evaluation of TR Systems   Multi Leve
 
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https://www.coursera.org/learn/text-retrieval
Views: 36 Ryo Eng
DATA MINING   2 Text Retrieval and Search Engines   Lesson 3 2 Evaluation of TR Systems   Basic Meas
 
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https://www.coursera.org/learn/text-retrieval
Views: 45 Ryo Eng
Link Analysis and PageRank (Information Retrieval)
 
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Link Analysis and PageRank (Information Retrieval)
Views: 3252 ashishsurekadelhi
K mean clustering algorithm with solve example
 
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Sample Notes : https://drive.google.com/file/d/19xmuQO1cprKqqbIVKcd7_-hILxF9yfx6/view?usp=sharing for notes fill the form : https://goo.gl/forms/C7EcSPmfOGleVOOA3 For full course:https://goo.gl/bYbuZ2 More videos coming soon so Subscribe karke rakho  :  https://goo.gl/85HQGm for full notes   please fill the form for notes :https://goo.gl/forms/MJD1mAOaTzyag64P2 For full hand made  notes of data warehouse and data mining  its only 200 rs payment options is PAYTM :7038604912 once we get payment notification we will mail you the notes on your email id contact us at :[email protected] For full course :https://goo.gl/Y1UcLd Topic wise: Introduction to Datawarehouse:https://goo.gl/7BnSFo Meta data in 5 mins :https://goo.gl/7aectS Datamart in datawarehouse :https://goo.gl/rzE7SJ Architecture of datawarehouse:https://goo.gl/DngTu7 how to draw star schema slowflake schema and fact constelation:https://goo.gl/94HsDT what is Olap operation :https://goo.gl/RYQEuN OLAP vs OLTP:https://goo.gl/hYL2kd decision tree with solved example:https://goo.gl/nNTFJ3 K mean clustering algorithm:https://goo.gl/9gGGu5 Introduction to data mining and architecture:https://goo.gl/8dUADv Naive bayes classifier:https://goo.gl/jVUNyc Apriori Algorithm:https://goo.gl/eY6Kbx Agglomerative clustering algorithmn:https://goo.gl/8ktMss KDD in data mining :https://goo.gl/K2vvuJ ETL process:https://goo.gl/bKnac9 FP TREE Algorithm:https://goo.gl/W24ZRF Decision tree:https://goo.gl/o3xHgo more videos coming soon so channel ko subscribe karke rakho
Views: 221889 Last moment tuitions
3D Spatial Data Mining on Document Sets
 
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The retrospective fault analysis of complex technical devices based on documents emerging in the advanced steps of the product life cycle can reveal error sources and problems, which have not been discovered by simulations or other test methods in the early stages of the product life cycle. This video presents a novel approach to support the failure analysis through (i) a semi-automatic analysis of databases containing product-related documents in natural language (e.g. problem and error descriptions, repair and maintenance protocols, service bills) using information retrieval and text mining techniques and (ii) an interactive exploration of the data mining results. Our system supports visual data mining by mapping the results of analyzing failure-related documents onto corresponding 3D models. Thus, visualization of statistics about failure sources can reveal problem sources resulting from problematic spatial configurations. This video can be found in high quality at wwwisg.cs.uni-magdeburg.de/~timo/videos/3DSpDataMining.avi The associated scientific publication available at wwwisg.cs.uni-magdeburg.de/~timo/ was published at the 2nd International Conference on Computer Graphics Theory and Applications (GRAPP'07)
Views: 8059 Graphenreiter
INTRODUCTION TO TEXT MINING IN HINDI
 
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find relevant notes at-https://viden.io/
Views: 6573 LearnEveryone
Data Mining- How to evaluate performance of an IR System
 
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Data Mining- How to evaluate performance of an IR System
Views: 301 John Paul
DATA MINING   2 Text Retrieval and Search Engines   Lesson 6 2 Learning to Rank Part 2
 
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https://www.coursera.org/learn/text-retrieval
Views: 12 Ryo Eng
Precision and Recall in 100 Seconds
 
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Precision and Recall explained with marker pens in 100 seconds. Do you want more videos about database topics explained in 100 seconds? Write a comment and propose a topic.
Views: 9514 InSy InSy