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MSBI - SSAS - Data Mining - SEQUENCE CLUSTERING
 
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MSBI - SSAS - Data Mining - SEQUENCE CLUSTERING
Views: 383 M R Dhandhukia
Pruning in Generalized Sequence Pattern (GSP) Algorithm
 
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This is additional material for Advanced Data Mining Class of WILP Students. It addresses pruning in GSP.
Views: 7296 Kamlesh Tiwari
Лекция 7. Deep Learning for Data with Sequence Structure
 
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Группа ВК: https://vk.com/data_mining_in_action Репозиторий курса на гитхабе: https://github.com/vkantor/MIPT_Data_Mining_In_Action_2016
Views: 1934 Data Mining in Action
Apriori Algorithm (Associated Learning) - Fun and Easy Machine Learning
 
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Apriori Algorithm (Associated Learning) - Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS Course - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Limited Time - Discount Coupon Apriori Algorithm The Apriori algorithm is a classical algorithm in data mining that we can use for these sorts of applications (i.e. recommender engines). So It is used for mining frequent item sets and relevant association rules. It is devised to operate on a database containing a lot of transactions, for instance, items brought by customers in a store. It is very important for effective Market Basket Analysis and it helps the customers in purchasing their items with more ease which increases the sales of the markets. It has also been used in the field of healthcare for the detection of adverse drug reactions. A key concept in Apriori algorithm is that it assumes that: 1. All subsets of a frequent item sets must be frequent 2. Similarly, for any infrequent item set, all its supersets must be infrequent too. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 58442 Augmented Startups
MSBI - SSAS - Data Mining - SEQUENCE CLUSTERING
 
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MSBI - SSAS - Data Mining - SEQUENCE CLUSTERING
Views: 128 Maulik Dhandhukia
SSAS - Data Mining - Decision Trees, Clustering, Neural networks
 
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SSAS - Data Mining - Decision Trees, Clustering, Neural networks
Views: 1291 M R Dhandhukia
Final Year Projects  | Mining Sequence Data & Time Series Data
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 345 Clickmyproject
2014 IEEE DATA MINING Learning Phenotype Structure Using Sequence Model
 
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To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - [email protected] Our Website: www.globalsofttechnologies.org
Similarity analysis of frequent sequential activity pattern mining
 
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One traveler's continuous GPS traces April 29 - Nov 4, 2013.
Data Mining, Classification, Clustering, Association Rules, Regression, Deviation
 
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Complete set of Video Lessons and Notes available only at http://www.studyyaar.com/index.php/module/20-data-warehousing-and-mining Data Mining, Classification, Clustering, Association Rules, Sequential Pattern Discovery, Regression, Deviation http://www.studyyaar.com/index.php/module-video/watch/53-data-mining
Views: 88268 StudyYaar.com
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: 212498 Well Academy
sequential pattern mining  and spade algorithm
 
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it describes about data mining , sequential pattern mining algorithm ,GSP , spade algorithm
Views: 3538 raj series
Mona Chalabi: Sequence, Sequence… Surprise! Designing Data for Maximum Impact
 
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As data editor for the Guardian US, Mona Chalabi contextualizes big numbers with her signature illustrated — and often provocative — data visualizations. But her end goal isn’t just a shareable image; it’s to make sure readers understand the big picture. In this talk, Chalabi shares her approach to storytelling with data, including: What common mistakes to avoid when presenting data Why data visualization can never be totally subjective How repetition and surprise can be wielded to emphasize important information About 99U 99U brings you the best of the creative world through the lens of design and the people and work who are shaping it.
Views: 4944 99U
Gosub - Last Sequence Complete
 
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Data Mining In Anthilia http://www.discogs.com/Gosub-Data-Mining-In-Anthilia/release/401784
Views: 5150 aquazone4
IEEE 2017-2018 DATA MINING EFFICIENT PATTERN BASED AGGREGATION ON SEQUENCE DATA
 
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PG Embedded Systems #197 B, Surandai Road Pavoorchatram,Tenkasi Tirunelveli Tamil Nadu India 627 808 Tel:04633-251200 Mob:+91-98658-62045 General Information and Enquiries: [email protected] [email protected] PROJECTS FROM PG EMBEDDED SYSTEMS 2017-2018 ieee projects, 2017-2018 ieee java projects, 2017-2018 ieee dotnet projects, 2017-2018 ieee android projects, 2017-2018 ieee matlab projects, 2017-2018 ieee embedded projects, 2017-2018 ieee robotics projects, 2017-2018 IEEE EEE PROJECTS, 2017-2018 IEEE POWER ELECTRONICS PROJECTS, ieee 2017-2018 android projects, ieee 2017-2018 java projects, ieee 2017-2018 dotnet projects, 2017-2018 ieee mtech projects, 2017-2018 ieee btech projects, 2017 -2018 ieee be projects, ieee 2017-2018 projects for cse, 2017-2018 ieee cse projects, 2017-2018 ieee it projects, 2017-2018 ieee ece projects, 2017-2018 ieee mca projects, 2017-2018 ieee mphil projects, tirunelveli ieee projects, best project centre in tirunelveli, bulk ieee projects, pg embedded systems ieee projects, pg embedded systems ieee projects, latest ieee projects, ieee projects for mtech, ieee projects for btech, ieee projects for mphil, ieee projects for be, ieee projects, student projects, students ieee projects, ieee proejcts india, ms projects, bits pilani ms projects, uk ms projects, ms ieee projects, ieee android real time projects, 2017-2018 mtech projects, 2017-2018 mphil projects, 2017-2018 ieee projects with source code, tirunelveli mtech projects, pg embedded systems ieee projects, ieee projects, 2017-2018 ieee project source code, journal paper publication guidance, conference paper publication guidance, ieee project, free ieee project, ieee projects for students., 2017-2018 ieee omnet++ projects, ieee 2017-2018 oment++ project, innovative ieee projects, latest ieee projects, 2017-2018 latest ieee projects, ieee cloud computing projects, 2017-2018 ieee cloud computing projects, 2017-2018 ieee networking projects, ieee networking projects, 2017-2018 ieee data mining projects, ieee data mining projects, 2017-2018 ieee network security projects, ieee network security projects, 2017-2018 ieee image processing projects, ieee image processing projects, ieee parallel and distributed system projects, ieee information security projects, 2017-2018 wireless networking projects ieee, 2017-2018 ieee web service projects, 2017-2018 ieee soa projects, ieee 2017-2018 vlsi projects, NS2 PROJECTS,NS3 PROJECTS.
Views: 3 GANESAN P
SPADE Algorithm
 
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Sequential pattern mining
Views: 9290 GRIETCSEPROJECTS
Deep Learning with Tensorflow - The Sequential Problem
 
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Enroll in the course for free at: https://bigdatauniversity.com/courses/deep-learning-tensorflow/ Deep Learning with TensorFlow Introduction The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this TensorFlow course you'll use Google's library to apply deep learning to different data types in order to solve real world problems. Traditional neural networks rely on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layer, or so-called more depth. These kind of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which is the vast majority of data in the world. TensorFlow is one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning. In this TensorFlow course, you will be able to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. Connect with Big Data University: https://www.facebook.com/bigdatauniversity https://twitter.com/bigdatau https://www.linkedin.com/groups/4060416/profile ABOUT THIS COURSE •This course is free. •It is self-paced. •It can be taken at any time. •It can be audited as many times as you wish. https://bigdatauniversity.com/courses/deep-learning-tensorflow/
Views: 9568 Cognitive Class
Sequence Bio Will Use AWS to Efficiently Store and Compute Data
 
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Sequence Bio uses AWS to host its data platform which will be used to analyze large amounts of data. Sequence Bio is a Newfoundland and Labrador biotechnology company that will use AWS to compute data to help identify patterns across disease and genetic groups to aid early stage drug discovery. Learn more: http://amzn.to/2rofAHf.
Views: 278 Amazon Web Services
Rule Base Classifier in Machine Learning in Hindi | Machine Learning Tutorials #7
 
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In this video we have explain the concept of Rule based Classifier in hindi Ml full notes rupees 200 only ML notes form : https://goo.gl/forms/7rk8716Tfto6MXIh1 Machine learning introduction : https://goo.gl/wGvnLg Machine learning #2 : https://goo.gl/ZFhAHd Machine learning #3 : https://goo.gl/rZ4v1f Linear Regression in Machine Learning : https://goo.gl/7fDLbA Logistic regression in Machine learning #4.2 : https://goo.gl/Ga4JDM decision tree : https://goo.gl/Gdmbsa K mean clustering algorithm : https://goo.gl/zNLnW5 Agglomerative clustering algorithmn : https://goo.gl/9Lcaa8 Apriori Algorithm : https://goo.gl/hGw3bY Naive bayes classifier : https://goo.gl/JKa8o2
Views: 10381 Last moment tuitions
Critical Analysis on how Data Mining Effects Individual Privacy
 
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In this critical analysis of data mining effects on individual privacy the topics that are focused on include marketing data mining, medical data mining, and privacy concerns and ethics about data mining. In sequence, this paper is organized as follows. Section 2 provides the background information and significance of data mining for the past and future. Section 3 opens the discussion with marketing data mining and how the Corporate Industrial Complex is already profiting off of data mining with no regards for individual privacy. Section 4 continues the discussion with medical data mining, a hot button issue for most Americans, by analyzing the current situation, looking at the need for data mining, and the possible threats to individual privacy. Section 5 ends the discussion with privacy concerns and ethics about data mining by comparing and contrasting the views of Americans and Europeans. Finally, Section 6 will summarize the paper and recap some of the main topic in the discussion about data mining effect on individual privacy. Tools used: Prezis Screenomatic basic upload to youtube
Views: 1056 Terry Henderson
Bioinformatics part 3 Sequence alignment introduction
 
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This Bioinformatics lecture explains the details about the sequence alignment. The mechanism and protocols of sequence alignment is explained in this video lecture on Bioinformatics. For more information, log on to- http://shomusbiology.weebly.com/ Download the study materials here- http://shomusbiology.weebly.com/bio-materials.html In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences.[1] Aligned sequences of nucleotide or amino acid residues are typically represented as rows within a matrix. Gaps are inserted between the residues so that identical or similar characters are aligned in successive columns. Sequence alignments are also used for non-biological sequences, such as those present in natural language or in financial data. Very short or very similar sequences can be aligned by hand. However, most interesting problems require the alignment of lengthy, highly variable or extremely numerous sequences that cannot be aligned solely by human effort. Instead, human knowledge is applied in constructing algorithms to produce high-quality sequence alignments, and occasionally in adjusting the final results to reflect patterns that are difficult to represent algorithmically (especially in the case of nucleotide sequences). Computational approaches to sequence alignment generally fall into two categories: global alignments and local alignments. Calculating a global alignment is a form of global optimization that "forces" the alignment to span the entire length of all query sequences. By contrast, local alignments identify regions of similarity within long sequences that are often widely divergent overall. Local alignments are often preferable, but can be more difficult to calculate because of the additional challenge of identifying the regions of similarity. A variety of computational algorithms have been applied to the sequence alignment problem. These include slow but formally correct methods like dynamic programming. These also include efficient, heuristic algorithms or probabilistic methods designed for large-scale database search, that do not guarantee to find best matches. Global alignments, which attempt to align every residue in every sequence, are most useful when the sequences in the query set are similar and of roughly equal size. (This does not mean global alignments cannot end in gaps.) A general global alignment technique is the Needleman--Wunsch algorithm, which is based on dynamic programming. Local alignments are more useful for dissimilar sequences that are suspected to contain regions of similarity or similar sequence motifs within their larger sequence context. The Smith--Waterman algorithm is a general local alignment method also based on dynamic programming. Source of the article published in description is Wikipedia. I am sharing their material. Copyright by original content developers of Wikipedia. Link- http://en.wikipedia.org/wiki/Main_Page
Views: 167321 Shomu's Biology
Leveraging Process Mining for Error Sequence Detection (Master Thesis)
 
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RWTH Aachen University - Software Engineering Group: http://www.se-rwth.de This screencast was created by Sebastian Napiorkowski during his Masterthesis "Leveraging Process Mining for Error Sequence Detection" supervised by Evgeny Kusmenko at the chair Software Engineering (Prof. Rumpe) and supervised by Evgeny Kusmenko and Bernhard Rumpe.
Efficient and Accurate Discovery of Patterns in Sequence Data Sets
 
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Efficient and Accurate Discovery of Patterns in Sequence Data Sets Abstract Existing sequence mining algorithms mostly focus on mining for subsequences. However, a large class of applications, such as biological DNA and protein motif mining, require efficient mining of "approximate" patterns that are contiguous. The few existing algorithms that can be applied to find such contiguous approximate pattern mining have drawbacks like poor scalability, lack of guarantees in finding the pattern, and difficulty in adapting to other applications. In this paper, we present a new algorithm called FLexible and Accurate Motif DEtector (FLAME). FLAME is a flexible suffix-tree-based algorithm that can be used to find frequent patterns with a variety of definitions of motif (pattern) models. It is also accurate, as it always finds the pattern if it exists. Using both real and synthetic data sets, we demonstrate that FLAME is fast, scalable, and outperforms existing algorithms on a variety of performance metrics. In addition, based on FLAME, we also address a more general problem, named extended structured motif extraction, which allows mining frequent combinations of motifs under relaxed constraints.
Views: 1481 eprotechnologies
Application of Data Mining in Bioinformatics
 
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CMPE 239 Presentation
Views: 1252 Vipul Kanade
Bioinformatics part 2 Databases (protein and nucleotide)
 
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For more information, log on to- http://shomusbiology.weebly.com/ Download the study materials here- http://shomusbiology.weebly.com/bio-materials.html This video is about bioinformatics databases like NCBI, ENSEMBL, ClustalW, Swisprot, SIB, DDBJ, EMBL, PDB, CATH, SCOPE etc. Bioinformatics Listeni/ˌbaɪ.oʊˌɪnfərˈmætɪks/ is an interdisciplinary field that develops and improves on methods for storing, retrieving, organizing and analyzing biological data. A major activity in bioinformatics is to develop software tools to generate useful biological knowledge. Bioinformatics uses many areas of computer science, mathematics and engineering to process biological data. Complex machines are used to read in biological data at a much faster rate than before. Databases and information systems are used to store and organize biological data. Analyzing biological data may involve algorithms in artificial intelligence, soft computing, data mining, image processing, and simulation. The algorithms in turn depend on theoretical foundations such as discrete mathematics, control theory, system theory, information theory, and statistics. Commonly used software tools and technologies in the field include Java, C#, XML, Perl, C, C++, Python, R, SQL, CUDA, MATLAB, and spreadsheet applications. In order to study how normal cellular activities are altered in different disease states, the biological data must be combined to form a comprehensive picture of these activities. Therefore, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data. This includes nucleotide and amino acid sequences, protein domains, and protein structures.[9] The actual process of analyzing and interpreting data is referred to as computational biology. Important sub-disciplines within bioinformatics and computational biology include: the development and implementation of tools that enable efficient access to, use and management of, various types of information. the development of new algorithms (mathematical formulas) and statistics with which to assess relationships among members of large data sets. For example, methods to locate a gene within a sequence, predict protein structure and/or function, and cluster protein sequences into families of related sequences. The primary goal of bioinformatics is to increase the understanding of biological processes. What sets it apart from other approaches, however, is its focus on developing and applying computationally intensive techniques to achieve this goal. Examples include: pattern recognition, data mining, machine learning algorithms, and visualization. Major research efforts in the field include sequence alignment, gene finding, genome assembly, drug design, drug discovery, protein structure alignment, protein structure prediction, prediction of gene expression and protein--protein interactions, genome-wide association studies, and the modeling of evolution. Bioinformatics now entails the creation and advancement of databases, algorithms, computational and statistical techniques, and theory to solve formal and practical problems arising from the management and analysis of biological data. Over the past few decades rapid developments in genomic and other molecular research technologies and developments in information technologies have combined to produce a tremendous amount of information related to molecular biology. Bioinformatics is the name given to these mathematical and computing approaches used to glean understanding of biological processes. Source of the article published in description is Wikipedia. I am sharing their material. Copyright by original content developers of Wikipedia. Link- http://en.wikipedia.org/wiki/Main_Page
Views: 96295 Shomu's Biology
Final Year Projects | Mining Sequence Data & Time Series Data
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-778-1155 +91 958-553-3547 +91 967-774-8277 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected] chat: http://support.elysiumtechnologies.com/support/livechat/chat.php
Views: 113 myproject bazaar
Social Sequence Analysis: An Overview
 
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Sandra Ham, a statistician from the Center for Health and the Social Sciences (CHeSS) at The University of Chicago, provides an introduction to the analytical method called social sequence analysis. Ham explores the history of the method, types of research questions that can be addressed using this method, types of data sources, capabilities, limitations and software options.
EXPLORATORY VISUAL SEQUENCE MINING BASED ON PATTERN-GROWTH
 
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EXPLORATORY VISUAL SEQUENCE MINING BASED ON PATTERN-GROWTH S/W: PYTHON, DJANGO
Views: 10 Chennai Sunday
Time Series Data Mining Forecasting with Weka
 
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I am sorry for my poor english. I hope it helps you. when i take the data mining course, i had searched it but i couldnt. So i decided to share this video with you.
Views: 24469 Web Educator
data mining - Mineração dados - Algoritmo Generalized Sequential Pattern (GSP)  (Portuguese)
 
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Neste video voce vai aprender sobre o algoritmo de mineração de dados Generalized Sequential Pattern (GSP), um algoritmo que cria associação de dados considerando o tempo, por exemplo, quem compra um celular, tende a comprar uma capa de celular, boa aula! @relation Compras @attribute idcliente {1,2,3,4,5,6,7} @attribute item {LEITE,OVOS,CAFE,ACUCAR,FRALDAS} @data 1, LEITE 1, OVOS 1, CAFE 2, LEITE 2, OVOS,FRALDAS 3, LEITE 3, FRALDAS 3, CAFE 4, OVOS 4, CAFE 4, ACUCAR 5, OVOS,FRALDAS 5, CAFE 6, CAFE 6, ACUCAR 6, FRALDAS 7, FRALDAS 7, CAFE,ACUCAR
Views: 450 Daniel Moraes
Time Series data Mining Using the Matrix Profile part 1
 
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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 1 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: 2534 KDD2017 video
Import Data and Analyze with MATLAB
 
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Data are frequently available in text file format. This tutorial reviews how to import data, create trends and custom calculations, and then export the data in text file format from MATLAB. Source code is available from http://apmonitor.com/che263/uploads/Main/matlab_data_analysis.zip
Views: 379660 APMonitor.com
Mining Patterns from Complex Datasets via Sampling by Dr. Zaki
 
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Symposium of Data Mining Applications on 8th of May 2014 Dr. Mohammed Zaki, Keynote Speaker
Views: 214 Megdam Center
Association Rule Mining in R
 
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This video is using Titanic data file that's embedded in R (see here: https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/Titanic.html). You can find both the data and the code here: https://github.com/A01203249/YouTube-Videos.git. Use git clone to clone this repo locally and use the code.
Views: 49258 Ani Aghababyan
Sequence 014 1 mining
 
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eok
Views: 28 Ted Smith
DSSS : Direct Sequence Spread Spectrum With Example Explained In Hindi
 
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FHSS : Frequency Hopping Spread Spectrum https://youtu.be/f4qblYxEEHM 📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 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) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING
Views: 16292 5 Minutes Engineering