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Constraints Based Frequent Pattern Mining ll All Constraints Explained in Hindi
 
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πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“ SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™ YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š
Views: 8468 5 Minutes Engineering
Data Mining Lecture -- Rule - Based Classification (Eng-Hindi)
 
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-~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 42788 Well Academy
Associative Classification ll Classification Using Frequent Patterns Explained in Hindi
 
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πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“ SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™ YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š
Views: 7654 5 Minutes Engineering
Mining Multilevel Association Rules ll DMW ll Concept Hierarchy ll Explained with Examples in Hindi
 
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πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“ SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™ YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š
Views: 18203 5 Minutes Engineering
What is CONSTRAINED CLUSTERING? What does CONSTRAINED CLUSTERING mean?
 
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What is CONSTRAINED CLUSTERING? What does CONSTRAINED CLUSTERING mean? CONSTRAINED CLUSTERING meaning - CONSTRAINED CLUSTERING definition - CONSTRAINED CLUSTERING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ In computer science, constrained clustering is a class of semi-supervised learning algorithms. Typically, constrained clustering incorporates either a set of must-link constraints, cannot-link constraints, or both, with a Data clustering algorithm. Both a must-link and a cannot-link constraint define a relationship between two data instances. A must-link constraint is used to specify that the two instances in the must-link relation should be associated with the same cluster. A cannot-link constraint is used to specify that the two instances in the cannot-link relation should not be associated with the same cluster. These sets of constraints acts as a guide for which a constrained clustering algorithm will attempt to find clusters in a data set which satisfy the specified must-link and cannot-link constraints. Some constrained clustering algorithms will abort if no such clustering exists which satisfies the specified constraints. Others will try to minimize the amount of constraint violation should it be impossible to find a clustering which satisfies the constraints. Constraints could also be used to guide the selection of a clustering model among several possible solutions. A cluster in which the members conform to all must-link and cannot-link constraints is called a chunklet.
Views: 525 The Audiopedia
Lecture - 16 Rule Based System
 
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Lecture Series on Artificial Intelligence by Prof.Sudeshna Sarkar and Prof.Anupam Basu, Department of Computer Science & Engineering,I.I.T, Kharagpur . For more details on NPTEL visit http://nptel.iitm.ac.in.
Views: 21234 nptelhrd
Market Basket Analysis And Frequent Patterns Explained with Examples in Hindi
 
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πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“ SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™ YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š
Views: 15174 5 Minutes Engineering
Machine Learning #81 Frequent Itemset Mining
 
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Machine Learning #81 Frequent Itemset Mining In this lecture of machine learning we are going to see frequent itemset mining. In frequent itemset mining tutorial we will see some examples of frequent itemset mining algorithm. Frequent itemset mining is a branch of data mining works by looking at sequences of events or action, for example the order in which a normal human being get dressed. Usually Shirt first? Pants first? Socks may be the second item or second shirt if its winter? In frequent itemset mining, the base data takes the form of sets of transactions that each has a number of items. Machine Learning Complete Tutorial/Lectures/Course from IIT (nptel) @ https://goo.gl/AurRXm Discrete Mathematics for Computer Science @ https://goo.gl/YJnA4B (IIT Lectures for GATE) Best Programming Courses @ https://goo.gl/MVVDXR Operating Systems Lecture/Tutorials from IIT @ https://goo.gl/GMr3if MATLAB Tutorials @ https://goo.gl/EiPgCF
Views: 920 Xoviabcs
Apriori Algorithm ll Generating Association Rules Explained With Example in Hindi
 
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Apriori Algorithm Part-1 https://youtu.be/WCK09hVXI9M Apriori Algorithm Explained With Solved Example Generating Association Rules. Association Rules Are Primary Aim or Output Of Apriori Algorithm. πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“ SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™ YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š
Views: 23297 5 Minutes Engineering
L3: Data Warehousing and Data Mining |Characteristics | Advantage | Evolution of Database Technology
 
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Join My official Whatsapp group by following link https://chat.whatsapp.com/F9XFi6QYFYOGA9JGw4gc1o L2: Data Warehousing and Data Mining |Characteristics | Advantage |Evolution of Database Technology Namaskar, In Today's lecture, i will cover Characteristics, Advantage, Evaluation of Database Technology of subject Data Warehousing and Data Mining which is one of the important subjects of computer science and engineering I am Sandeep Vishwakarma (www.universityacademy.in) from Raj Kumar Goel Institute of Technology Ghaziabad. I have started a YouTube Channel Namely β€œUniversity Academy” Teaching Training and Informative. On This channel am providing following services. 1 . Teaching: Video Lecture of B.Tech./ M.Tech. Technical Subject who provide you deep knowledge of particular subject. Compiler Design: https://www.youtube.com/playlist?list=PL-JvKqQx2Ate5DWhppx-MUOtGNA4S3spT Principle of Programming Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdIkEFDrqsHyKWzb5PWniI1 Theory of Automata and Formal Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdhlS7j6jFoEnxmUEEsH9KH 2. Training: Video Playlist of Some software course like Android, Hadoop, Big Data, IoT, R programming, Python, C programming, Java etc. Android App Development: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdBj8aS-3WCVgfQ3LJFiqIr 3. Informative: On this Section we provide video on deep knowledge of upcoming technology, Innovation, tech news and other informative. Tech News: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdFG5kMueyK5DZvGzG615ks Other: https://www.youtube.com/playlist?list=PL-JvKqQx2AtfQWfBddeH_zVp2fK4V5orf Download You Can Download All Video Lecture, Lecture Notes, Lab Manuals and Many More from my Website: http://www.universityacademy.in/ Regards University Academy UniversityAcademy Website: http://www.universityacademy.in/ YouTube: https://www.youtube.com/c/UniversityAcademy Facebook: https://www.facebook.com/UniversityAcademyOfficial Twitter https://twitter.com/UniAcadofficial Instagram https://www.instagram.com/universityacademyofficial Google+: https://plus.google.com/+UniversityAcademy
Views: 570 University Academy
The Apriori Algorithm ... How The Apriori Algorithm Works
 
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My web page: www.imperial.ac.uk/people/n.sadawi
Views: 164247 Noureddin Sadawi
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: 123915 StudyKorner
Generating Association rules
 
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Once the Frequent itemsets are mined, Association rules has to be generated.
Views: 2030 Dakshina Kumaresan
Mega-R1. Rule-Based Systems
 
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MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Mark Seifter In this mega-recitation, we cover Problem 1 from Quiz 1, Fall 2009. We begin with the rules and assertions, then spend most of our time on backward chaining and drawing the goal tree for Part A. We end with a brief discussion of forward chaining. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 28123 MIT OpenCourseWare
Generating Association Rules from Frequent Itemsets
 
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My web page: www.imperial.ac.uk/people/n.sadawi
Views: 72279 Noureddin Sadawi
Repeated HoldOut Method ll Evaluating The Classifier ll Overlapping Test Set Problem Explained
 
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πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“ SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™ YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š
Views: 5159 5 Minutes Engineering
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.
Decision Tree Solved | Id3 Algorithm (concept and numerical) | Machine Learning (2019)
 
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Decision Tree is a supervised learning method used for classification and regression. It is a tree which helps us by assisting us in decision-making! Decision tree builds classification or regression models in the form of a tree structure. It breaks down a data set into smaller and smaller subsets and simultaneously decision tree is incrementally developed. The final tree is a tree with decision nodes and leaf nodes. A decision node has two or more branches. Leaf node represents a classification or decision. We cannot do more split on leaf nodes. The topmost decision node in a tree which corresponds to the best predictor called root node. Decision trees can handle both categorical and numerical data. #codewrestling #decisiontree #machinelearning #id3 Common terms used with Decision trees: Root Node: It represents entire population or sample and this further gets divided into two or more homogeneous sets. Splitting: It is a process of dividing a node into two or more sub-nodes. Decision Node: When a sub-node splits into further sub-nodes, then it is called decision node. Leaf/ Terminal Node: Nodes do not split is called Leaf or Terminal node. Pruning: When we remove sub-nodes of a decision node, this process is called pruning. You can say opposite process of splitting. Branch / Sub-Tree: A sub section of entire tree is called branch or sub-tree. Parent and Child Node: A node, which is divided into sub-nodes is called parent node of sub-nodes whereas sub-nodes are the child of parent node. How does Decision Tree works ? Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It works for both categorical and continuous input and output variables. In this technique, we split the population or sample into two or more homogeneous sets (or sub-populations) based on most significant splitter / differentiator in input variables. Advantages of Decision Tree: 1. Easy to Understand: Decision tree output is very easy to understand even for people from non-analytical background. It does not require any statistical knowledge to read and interpret them. Its graphical representation is very intuitive and users can easily relate their hypothesis. 2. Useful in Data exploration: Decision tree is one of the fastest way to identify most significant variables and relation between two or more variables. With the help of decision trees, we can create new variables / features that has better power to predict target variable. It can also be used in data exploration stage. For e.g., we are working on a problem where we have information available in hundreds of variables, there decision tree will help to identify most significant variable. 3 Decision trees implicitly perform variable screening or feature selection. 4. Decision trees require relatively little effort from users for data preparation. 5. Less data cleaning required: It requires less data cleaning compared to some other modeling techniques. It is not influenced by outliers and missing values to a fair degree. 6. Data type is not a constraint: It can handle both numerical and categorical variables. Can also handle multi-output problems. ID3 Algorithm Key Factors: Entropy- It is the measure of randomness or β€˜impurity’ in the dataset. Information Gain: It is the measure of decrease in entropy after the dataset is split. Ask me A Question: [email protected] Music: https://www.bensound.com For Decision Trees slides comment below πŸ˜€
Views: 1660 Code Wrestling
Machine Learning #82 Apriori Property | Frequent Itemset Mining
 
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Machine Learning #82 Apriori Property Explained with Example | Frequent Itemset Mining. In this lecture of machine learning we are going to learn about apriori property in detail with example. Apriori Property is used for frequent itemset mining. The Apriori property is the property which shows that values of evaluation criteria of sequential patterns are smaller than or equal to those of their sequential subpatterns. Machine Learning Complete Tutorial/Lectures/Course from IIT (nptel) @ https://goo.gl/AurRXm Discrete Mathematics for Computer Science @ https://goo.gl/YJnA4B (IIT Lectures for GATE) Best Programming Courses @ https://goo.gl/MVVDXR Operating Systems Lecture/Tutorials from IIT @ https://goo.gl/GMr3if MATLAB Tutorials @ https://goo.gl/EiPgCF
Views: 479 Xoviabcs
FP Growth Algorithm ll DMW llConditional Pattern Base Explained with Solved Example in Hindi
 
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FP Tree Construction https://youtu.be/GzRi6pbdQ2E πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“ SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™ YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š
Views: 15263 5 Minutes Engineering
Association rule learning
 
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Please Subscribe our goal is 5000 subscriber for this year :) In data mining, association rule learning is a popular and well researched method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using different measures of interestingness. Source:http://en.wikipedia.org/wiki/Association_rule_learning
Views: 519 Wikivoicemedia
Mining Multidimensional Hybrid Association Rules  Boolean Matrix Dotnet project
 
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ePRoSOFT Technologies,Kharghar-Navi Mumbai. 02266730140/9321060440/9769890003 [email protected]/[email protected] 5 min. walk from Kharghar station.
Views: 296 ePRoSOFToProjects
FP Growth Algorithm ll DMW ll Conditional FP Tree Explained with Solved Example in Hindi
 
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πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“ SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™ YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š
Views: 12882 5 Minutes Engineering
Market Basket Analysis
 
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Views: 646 Disha Khanna
Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods By Kelompok NOB
 
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Valen Orlando Muhammad Zakka Syahran Rizky Akhya Brando Beny Nofendra
Views: 1648 Sinanju Stein
Decision Tree Algorithm Explained With Example ll DMW ll ML Easiest Explanation Ever in Hindi
 
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Decision Tree Algorithm Part 2 https://youtu.be/ffZ0ShPi-wg πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“ SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™ YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š Decision Tree Algorithm DMW Data Mining And Warehousing Information Gain Entropy formula Gain Formula Decision Tree Solved Example
Views: 47854 5 Minutes Engineering
Declarative Pattern Mining Using Constraint Programming
 
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Presentation for the ACP Doctoral Research Award by Tias Guns (KULeuven) at CP 2013 (http://cp2013.a4cp.org) held September 16-20, 2013 in Uppsala, Sweden
Frequent Pattern Mining
 
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Project Name: Learning by Doing (LBD) based course content development Project Investigator: Prof Sandhya Kode
Views: 4778 Vidya-mitra
Managing Categorial Data Explained with Examples in Hindi ll Machine Learning Course
 
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πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“ SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™ YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š
Views: 7985 5 Minutes Engineering
Data Mining Update - Reassessing Redundancy
 
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I have thought over what I planned to do on the previous stream, and have decided to make a few changes in how I will tackle this project.
Views: 13 JonoExplainsThings
Frequent Itemsets Mining With Differential Privacy Over Large-Scale Data
 
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Frequent Itemsets Mining With Differential Privacy Over Large-Scale Data To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9. Mobile: (0)9952649690, Email: [email protected], Website: https://www.jpinfotech.org Frequent itemsets mining with differential privacy refers to the problem of mining all frequent itemsets whose supports are above a given threshold in a given transactional dataset, with the constraint that the mined results should not break the privacy of any single transaction. Current solutions for this problem cannot well balance efficiency, privacy, and data utility over large-scale data. Toward this end, we propose an efficient, differential private frequent itemsets mining algorithm over large-scale data. Based on the ideas of sampling and transaction truncation using length constraints, our algorithm reduces the computation intensity, reduces mining sensitivity, and thus improves data utility given a fixed privacy budget. Experimental results show that our algorithm achieves better performance than prior approaches on multiple datasets.
Views: 56 jpinfotechprojects
Machine Learning #73 BIRCH Algorithm | Clustering
 
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Machine Learning #73 BIRCH Algorithm | Clustering In this lecture of machine learning we are going to see BIRCH algorithm for clustering with example. BIRCH algorithm (balanced iterative reducing and clustering using hierarchies) is an unsupervised data mining algorithm which is used to perform hierarchical clustering over particularly large data-sets.The advantage of using BIRCH algorithm is its ability to incrementally & dynamically cluster incoming, multi-dimensional metric data points in an attempt to produce the best quality clustering for a given set of resources (memory and time constraints). single scan of the database is needed by BIRCH algorithm in most of the cases. Machine Learning Complete Tutorial/Lectures/Course from IIT (nptel) @ https://goo.gl/AurRXm Discrete Mathematics for Computer Science @ https://goo.gl/YJnA4B (IIT Lectures for GATE) Best Programming Courses @ https://goo.gl/MVVDXR Operating Systems Lecture/Tutorials from IIT @ https://goo.gl/GMr3if MATLAB Tutorials @ https://goo.gl/EiPgCF
Views: 12391 Xoviabcs
Ashutosh Jadhav: Knowledge-driven Search Intent Mining
 
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http://www.knoesis.org/aboutus/thesis_defense#jadhav ABSTRACT: Understanding users’ latent intents behind search queries is essential for satisfying a user’s search needs. Search intent mining can help search engines to enhance its ranking of search results, enabling new search features like instant answers, personalization, search result diversification, and the recommendation of more relevant ads. Consequently, there has been increasing attention on studying how to effectively mine search intents by analyzing search engine query logs. While state-of-the-art techniques can identify the domain of the queries (e.g. sports, movies, health), identifying domain-specific intent is still an open problem. Among all the topics available on the Internet, health is one of the most important in terms of impact on the user and it is one of the most frequently searched areas. This dissertation presents a knowledge-driven approach for domain-specific search intent mining with a focus on health-related search queries. First, we identified 14 consumer-oriented health search intent classes based on inputs from focus group studies and based on analyses of popular health websites, literature surveys, and an empirical study of search queries. We defined the problem of classifying millions of health search queries into zero or more intent classes as a multi-label classification problem. Popular machine learning approaches for multi-label classification tasks (namely, problem transformation and algorithm adaptation methods) were not feasible due to the limitation of label data creations and health domain constraints. Another challenge in solving the search intent identification problem was mapping terms used by laymen to medical terms. To address these challenges, we developed a semantics-driven, rule-based search intent mining approach leveraging rich background knowledge encoded in Unified Medical Language System (UMLS) and a crowd sourced encyclopedia (Wikipedia). The approach can identify search intent in a disease-agnostic manner and has been evaluated on three major diseases. While users often turn to search engines to learn about health conditions, a surprising amount of health information is also shared and consumed via social media, such as public social platforms like Twitter. Although Twitter is an excellent information source, the identification of informative tweets from the deluge of tweets is the major challenge. We used a hybrid approach consisting of supervised machine learning, rule-based classifiers, and biomedical domain knowledge to facilitate the retrieval of relevant and reliable health information shared on Twitter in real time. Furthermore, we extended our search intent mining algorithm to classify health-related tweets into health categories. Finally, we performed a large-scale study to compare health search intents and features that contribute in the expression of search intent from more than 100 million search queries from smarts devices (smartphones or tablets) and personal computers (desktops or laptops). SLIDES: http://www.slideshare.net/knoesis/ashutosh-thesis
Views: 200 Knoesis Center
Data Mining Functionalities || Data Characterization & Data Discrimination || Lecture In Urdu/Hindi
 
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What are data mining functionalities? Data characterization and data Discrimination
Views: 10169 Focus Group
Data Mining & Business Intelligence | Tutorial #26 | OPTICS
 
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Order my books at πŸ‘‰ http://www.tek97.com/ #RanjiRaj #DataMining #OPTICS Follow me on Instagram πŸ‘‰ https://www.instagram.com/reng_army/ Visit my Profile πŸ‘‰ https://www.linkedin.com/in/reng99/ Support my work on Patreon πŸ‘‰ https://www.patreon.com/ranjiraj OPTICS is a density based clustering technique in data mining for identifying arbitrary shaped clusters. Watch Now ! OPTICS Ω‡ΩŠ ΨͺΩ‚Ω†ΩŠΨ© ΨͺΨ¬Ω…ΩŠΨΉ ΨͺΨΉΨͺΩ…Ψ― ΨΉΩ„Ω‰ الكثافة في Ψ§Ω„ΨͺΩ†Ω‚ΩŠΨ¨ ΨΉΩ† Ψ§Ω„Ψ¨ΩŠΨ§Ω†Ψ§Ψͺ Ω„Ψͺحديد Ψ§Ω„Ω…Ψ¬Ω…ΩˆΨΉΨ§Ψͺ Ψ§Ω„ΨΉΨ΄ΩˆΨ§Ψ¦ΩŠΨ©. Ψ΄Ψ§Ω‡Ψ― Ψ§Ω„Ψ’Ω† ! ОПВИКА - это ΠΌΠ΅Ρ‚ΠΎΠ΄ кластСризации Π½Π° основС плотности ΠΏΡ€ΠΈ Π΄ΠΎΠ±Ρ‹Ρ‡Π΅ Π΄Π°Π½Π½Ρ‹Ρ… для ΠΈΠ΄Π΅Π½Ρ‚ΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΠΈ кластСров ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ»ΡŒΠ½ΠΎΠΉ Ρ„ΠΎΡ€ΠΌΡ‹. Π‘ΠΌΠΎΡ‚Ρ€ΠΈ ! OPTICS es una tΓ©cnica de agrupaciΓ³n basada en la densidad en la minerΓ­a de datos para identificar clusters con formas arbitrarias. Ver ahora ! OPTICS ist eine dichte-basierte Clustering-Technik im Data Mining zur Identifizierung beliebig geformter Cluster. Schau jetzt ! OPTICS est une technique de clustering basΓ©e sur la densitΓ© dans l'exploration de donnΓ©es pour identifier des groupes de formes arbitraires. Regarde maintenant ! OPTICS Γ© uma tΓ©cnica de clustering baseada em densidade em mineração de dados para identificar clusters de forma arbitrΓ‘ria. Assista agora ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook πŸ‘‰https://www.facebook.com/renji.nair.09 Follow me on TwitterπŸ‘‰https://twitter.com/iamRanjiRaj Read my StoryπŸ‘‰https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my ProfileπŸ‘‰https://www.linkedin.com/in/reng99/ Like TheStudyBeast on FacebookπŸ‘‰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: 3161 Ranji Raj
Data Cleaning Process Steps / Phases [Data Mining] Easiest Explanation Ever (Hindi)
 
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πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“ SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™ YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5MINUTES ENGINEERING πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š
Views: 23941 5 Minutes Engineering
Text and Data Mining  - November 2017
 
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β€œTDM is any automated analytical technique aiming to analyze text and data in digital form in order to generate information such as patterns, trends, and correlations.” --European Commission
DBMS - Specialization and Generalization
 
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DBMS - Specialization and Generalization Watch more Videos at https://www.tutorialspoint.com/videotutorials/index.htm Lecture By: Mr. Arnab Chakraborty, Tutorials Point India Private Limited