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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: 31181 Last Minute Tutorials
Introduction to data mining and architecture  in hindi
 
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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 200rs 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: 151731 Last moment tuitions
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: 202636 Thales Sehn Körting
What is Data Mining?
 
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NJIT School of Management professor Stephan P Kudyba describes what data mining is and how it is being used in the business world.
Views: 374005 YouTube NJIT
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 MINING EXPLAINED IN HINDI | "ITNA SARA DATA??"
 
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नमस्कार दोस्तों,आज की वीडियो में में आप सभी को DATA MINING के बारे में बताने जा रहा हूँ की आखिर DATA MINING क्या होती है और क्या ये हमारे किसी काम आती हैं या नहीं और आखिर हमारे ज़िन्दगी में इसकी कितनी जरुरत है। आशा करता हूँ आपको ये वीडियो पसंद आएगी अगर आपको वीडियो पसंद आये तो वीडियो को LIKE SHARE और चैनल को SUBSCRIBE जरूर से करे। धन्यवाद। जय हिन्द वन्दे मातरम subscribe our channel on youtube: https://www.youtube.com/channel/UCR_kAPwG59SxWRaUfzk3qoQ facebook: https://www.facebook.com/dropouttechnical/ twitter: https://twitter.com/dropoutechnical google+: https://plus.google.com/u/0/103031877017890269380 -~-~~-~~~-~~-~- Please watch: "MOTO X4 my opinion |"Best phone??"|"worth buy at 21000"🔥" https://www.youtube.com/watch?v=r54C6_667uU -~-~~-~~~-~~-~-
Views: 8125 Dropout Technical
▶ Application of Data Mining - Real Life Use of Data Mining - Where We Can Use Data Mining ?
 
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Data Mining becomes a very hot topic in this moments because of its various uses. We can apply data mining to predict about an event that might happen. ✔Application of Data Mining - Real Life Use of Data Mining - Where We Can Use Data Mining? We're gonna learn some real-life scenario of Data Mining in this video. »See Full #Data_Mining Video Series Here: https://www.youtube.com/watch?v=t8lSMGW5eT0&list=PL9qn9k4eqGKRRn1uBmEhlmEd58ATOziA1 In This Video You are gonna learn Data Mining #Bangla_Tutorial Data mining is an important process to discover knowledge about your customer behavior towards your business offerings. » My #Linkedin_Profile: https://www.linkedin.com/in/rafayet13 » Read My Full Article on #Data_Mining Career Opportunity & So On » Link: https://medium.com/@rafayet13 #Learn_Data_Mining_In_A_Easy_Way #Data_Mining_Essential_Course #Data_Mining_Course_For_Beginner ট্র্যাডিশনাল পদ্ধতিতে যে সকল সমস্যার সহজে কোন সমাধান দেয়া যায় না #ডেটা_মাইনিং ব্যবহারে সহজেই একটি সিদ্ধান্তে পৌঁছানো সম্ভব। আর সে সিদ্ধান্ত কাজে লাগিয়ে ব্যবসায়িক অথবা যে কোন সম্পর্কিত সিদ্ধান্ত গ্রহন সম্ভব। Data Mining,big data,data analysis,data mining tutorial,book bd,Bangla tutorials,data mining software,Data Mining,What is data mining,bookbd,data analysis,data mining tutorial,data science,big data, business intelligence,data mining tools,bangla tutorial,data mining bangla tutorial,how to,how to mine data, knowledge discovery, Artificial Intelligence,Deep learning,machine learning,Python tutorials, Data Mining in the Retail Industry What does the future of business look like? How data will transform business? How data mining will transform business?
Views: 6482 BookBd
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: 2743 SlideTalk
INTRODUCTION TO DATA MINING IN HINDI
 
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Buy Software engineering books(affiliate): Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2whY4Ke Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2wfEONg Software Engineering: A Practitioner's Approach (India) by McGraw-Hill Higher Education https://amzn.to/2PHiLqY Software Engineering by Pearson Education https://amzn.to/2wi2v7T Software Engineering: Principles and Practices by Oxford https://amzn.to/2PHiUL2 ------------------------------- find relevant notes at-https://viden.io/
Views: 101173 LearnEveryone
An Example Application of Data Mining
 
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Have a look at one of our decision support systems powered by our data mining algorithms.
Data mining issues and challenges. By game fan
 
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Describe what is data mining and its issues step by step Game fan Gamefan
Views: 1870 K game fan
Data Mining (Introduction for Business Students)
 
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This short revision video introduces the concept of data mining. Data mining is the process of analysing data from different perspectives and summarising it into useful information, including discovery of previously unknown interesting patterns, unusual records or dependencies. There are many potential business benefits from effective data mining, including: Identifying previously unseen relationships between business data sets Better predicting future trends & behaviours Extract commercial (e.g. performance insights) from big data sets Generating actionable strategies built on data insights (e.g. positioning and targeting for market segments) Data mining is a particularly powerful series of techniques to support marketing competitiveness. Examples include: Sales forecasting: analysing when customers bought to predict when they will buy again Database marketing: examining customer purchasing patterns and looking at the demographics and psychographics of customers to build predictive profiles Market segmentation: a classic use of data mining, using data to break down a market into meaningful segments like age, income, occupation or gender E-commerce basket analysis: using mined data to predict future customer behavior by past performance, including purchases and preferences
Views: 2102 tutor2u
GTU Research Week Workshop by Dr. S Nickolas : Data Mining & its Applications
 
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GTU Research Week 2015 Workshop conducted by Dr. S Nickolas on Data Mining & its Applications at Gujarat Technological University, Chandkheda, Ahmedabad
What is TEXT MINING? What does TEXT MINING mean? TEXT MINING meaning, definition & explanation
 
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What is TEXT MINING? What does TEXT MINING mean? TEXT MINING meaning - TEXT MINING definition - TEXT MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interestingness. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities). Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics. The overarching goal is, essentially, to turn text into data for analysis, via application of natural language processing (NLP) and analytical methods. A typical application is to scan a set of documents written in a natural language and either model the document set for predictive classification purposes or populate a database or search index with the information extracted. The term text analytics describes a set of linguistic, statistical, and machine learning techniques that model and structure the information content of textual sources for business intelligence, exploratory data analysis, research, or investigation. The term is roughly synonymous with text mining; indeed, Ronen Feldman modified a 2000 description of "text mining" in 2004 to describe "text analytics." The latter term is now used more frequently in business settings while "text mining" is used in some of the earliest application areas, dating to the 1980s, notably life-sciences research and government intelligence. The term text analytics also describes that application of text analytics to respond to business problems, whether independently or in conjunction with query and analysis of fielded, numerical data. It is a truism that 80 percent of business-relevant information originates in unstructured form, primarily text. These techniques and processes discover and present knowledge – facts, business rules, and relationships – that is otherwise locked in textual form, impenetrable to automated processing.
Views: 1800 The Audiopedia
Healthcare Data Mining with Matrix Models (Part 1)
 
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Authors: Joel Dudley, Icahn School of Medicine at Mount Sinai Ping Zhang, IBM Thomas J. Watson Research Center Fei Wang, Department of Healthcare Policy and Research, Cornell University Abstract: In the last decade, advances in high-throughput technologies, growth of clinical data warehouses, and rapid accumulation of biomedical knowledge provided unprecedented opportunities and challenges to researchers in biomedical informatics. One distinct solution, to efficiently conduct big data analytics for biomedical problems, is the application of matrix computation and factorization methods such as non-negative matrix factorization, joint matrix factorization, tensor factorization. Compared to probabilistic and information theoretic approaches, matrix-based methods are fast, easy to understand and implement. In this tutorial, we provide a review of recent advances in algorithms and methods using matrix and their potential applications in biomedical informatics. We survey various related articles from data mining venues as well as from biomedical informatics venues to share with the audience key problems and trends in matrix computation research, with different novel applications such as drug repositioning, personalized medicine, and electronic phenotyping. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 634 KDD2016 video
The ART of Data Mining – Practical learnings from real-world data mining applications
 
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Machine Learning and data mining is part SCIENCE (ML algorithms, optimization), part ENGINEERING (large-scale modelling, real-time decisions), part PROCESS (data understanding, feature engineering, modelling, evaluation, and deployment), and part ART. In this talk, Dr. Shailesh Kumar focuses on the "ART of data mining" - the little things that make the big difference in the quality and sophistication of machine learning models we build. Using real-world analytics problems from a variety of domains, Shailesh shares a number of practical learnings in: (1) The art of understanding the data better - (e.g. visualization of text data in a semantic space) (2) The art of feature engineering - (e.g. converting raw inputs into meaningful and discriminative features) (3) The art of dealing with nuances in class labels - (e.g. creating, sampling, and cleaning up class labels) (4) The art of combining labeled and unlabelled data - (e.g. semi-supervised and active learning) (5) The art of decomposing a complex modelling problem into simpler ones - (e.g. divide and conquer) (6) The art of using textual features with structured features to build models, etc. The key objective of the talk is to share some of the learnings that might come in handy while "designing" and "debugging" machine learning solutions and to give a fresh perspective on why data mining is still mostly an ART.
Views: 1682 HasGeek TV
DATA MINING | The Checkout | ABC1
 
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Kirsten Drysdale finds out how retailers knew a teenager was pregnant before her parents did, in a story about the way our data is collected and used. How viewers can get involved in THE CHECKOUT: http://facebook.com/checkouttv http://twitter.com/checkouttv #thecheckout http://futube.net.au (where you can send in video complaints) [email protected] (email us directly)
Views: 108384 The Checkout
Introduction to Datawarehouse in hindi | Data warehouse and data mining Lectures
 
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#datawarehouse #datamining #lastmomenttuitions 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: 219210 Last moment tuitions
#FixCopyright:  Copyright & Research - Text & Data Mining (TDM) Explained
 
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Read our blog post analysing the European Commission's (EC) text and data mining (TDM) exception and providing recommendations on how to improve it: http://bit.ly/2cE60sp Copy (short for Copyright) explains what text and data mining (TDM) is all about, and what hurdles researchers are currently facing. We also have a blog post on the TDM bits in the EC's Impact Assessment accompanying the proposal: http://bit.ly/2du9sYe Read more about the EC's copyright reform proposals in general: http://bit.ly/2cvAh0a
Views: 3085 FixCopyright
An Introduction to Linear Regression Analysis
 
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Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class. Playlist on Linear Regression http://www.youtube.com/course?list=ECF596A4043DBEAE9C Like us on: http://www.facebook.com/PartyMoreStudyLess Created by David Longstreet, Professor of the Universe, MyBookSucks http://www.linkedin.com/in/davidlongstreet
Views: 629966 statisticsfun
INTRODUCTION TO CLASSIFICATION - DATA MINING
 
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Classification consists of predicting a certain outcome based on a given input. In order to predict the outcome, the algorithm processes a training set containing a set of attributes and the respective outcome, usually called goal or prediction attribute. The algorithm tries to discover relationships between the attributes that would make it possible to predict the outcome. Next the algorithm is given a data set not seen before, called prediction set, which contains the same set of attributes, except for the prediction attribute – not yet known. The algorithm analyses the input and produces a prediction.
Views: 31593 Nina Canares
Data Mining Techniques
 
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Decision Trees, Naive Bayes, and Neural Networks
Views: 21026 nathan baughman
Google Analytics Data Mining with R (includes 3 Real Applications)
 
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R is already a Swiss army knife for data analysis largely due its 6000 libraries but until now it lacked an interface to the Google Analytics API. The release of RGoogleAnalytics library solves this problem. What this means is that digital analysts can now fully use the analytical capabilities of R to fully explore their Google Analytics Data. In this webinar, Andy Granowitz, ‎Developer Advocate (Google Analytics) & Kushan Shah, Contributor & maintainer of RGoogleAnalytics Library will show you how to use R for Google Analytics data mining & generate some great insights. Useful Resources:http://bit.ly/r-googleanalytics-resources
Views: 28163 Tatvic Analytics
Datamining in Science: Mining Patterns in Protein StructuresΓÇöAlgorithms and Applications
 
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With the data explosion occurring in sciences, utilizing tools to help analyze the data efficiently is becoming increasingly important. This session will describe tools included with SQL Server (Yukon), and Wei Wang will describe the MotifSpace projectΓÇöa comprehensive database of candidate spatial protein motifs based on recently developed data mining algorithms. One of the next great frontiers in molecular biology is to understand and predict protein function. Proteins are simple linear chains of polymerized amino acids (residues) whose biological functions are determined by the three-dimensional shapes that they fold into. A popular approach to understanding proteins is to break them down into structural sub-components called motifs. Motifs are recurring structural and spatial units that are frequently correlated with specific protein functions. Traditionally, the discovery of motifs has been a laborious task of scientific exploration. In this talk, I will discuss recent data-mining algorithms that we have developed for automatically identifying potential spatial motifs. Our methods automatically find frequently occurring substructures within graph-based representations of proteins. The complexity of protein structures and corresponding graphs poses significant computational challenges. The kernel of our approach is an efficient subgraph-mining algorithm that detects all (maximal) frequent subgraphs from a graph database with a user-specified minimal frequency.
Views: 100 Microsoft Research
Datamining techniques 2013 Assignment 2 part E, datamining application
 
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For Datamining techniques we had to pick an application that uses datamining techniques with succes. We choose weatherbug, as the weather is a well studied phenomenon and data mining is used to get up to date forecasts. First time video made with windowsmovie maker Disclaimer: all rights of the borrowed materials are not mine
Views: 55 Trophilya
Data Mining Lecture - - Advance Topic | Web mining | Text mining (Eng-Hindi)
 
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Data mining Advance topics - Web mining - Text Mining -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~- Follow us on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 42215 Well Academy
Machine Learning and Data Mining Solutions
 
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Oodles Technologies offer best-in-class Machine Learning and Data Mining #SoftwareSolutions for a wide range of applications. We thrive on delivering best quality services at reasonable and cost-effective rates. We have a team of expert developers specializing in #MachineLearning, #ArtificialIntelligence, #BigData, #DataMining and #ComputerVision. Contact us now :- http://www.oodlestechnologies.com/machine-learning-and-data-mining-software-solutions
Data Mining and Business Intelligence for Cyber Security Applications Summer Program at BGU
 
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The purpose of the Summer Program in Data Mining and Business Intelligence is to provide both theoretical and practical knowledge, including tools, on data mining. The program offers two academic courses (each for 3 credits), where students learn the basic tools of data mining and the utilization of machine learning techniques for solving cyber security problems. The program includes a mandatory one week internship at BGU’s Cyber Security Research Center. The internship corresponds with the course materials and contributes the practical experience component. In addition, students will take part in professional fieldtrips to leading companies, in order to enhance their understanding of data mining and cyber security To Apply: https://www.tfaforms.com/399172 For More information: www.bgu.ac.il/global
Views: 1120 BenGurionUniversity
data mining in healthcare
 
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how does the data mining technique help in solving healthcare problem-- Created using PowToon -- Free sign up at http://www.powtoon.com/ . Make your own animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 5885 Fouz Alaseeri
THE EFFECTIVENESS OF DATA MINING TECHNIQUES IN BANKING
 
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Computer Applications: An International Journal (CAIJ) ISSN :2393 - 8455 http://airccse.com/caij/index.html ********************************************* Computer Applications: An International Journal (CAIJ), Vol.4, No.1/2/3/4, November 2017 DOI:10.5121/caij.2017.4401 THE EFFECTIVENESS OF DATA MINING TECHNIQUES IN BANKING Yuvika Priyadarshini Researcher, Jharkhand Rai University, Ranchi. ABSTRACT The aim of this study is to identify the extent of Data mining activities that are practiced by banks, Data mining is the ability to link structured and unstructured information with the changing rules by which people apply it. It is not a technology, but a solution that applies information technologies. Currently several industries including like banking, finance, retail, insurance, publicity, database marketing, sales predict, etc are Data Mining tools for Customer . Leading banks are using Data Mining tools for customer segmentation and benefit, credit scoring and approval, predicting payment lapse, marketing, detecting illegal transactions, etc. The Banking is realizing that it is possible to gain competitive advantage deploy data mining. This article provides the effectiveness of Data mining technique in organized Banking. It also discusses standard tasks involved in data mining; evaluate various data mining applications in different sectors KEYWORDS Definition of Data Mining and its task, Effectiveness of Data Mining Technique, Application of Data Mining in Banking, Global Banking Industry Trends, Effective Data Mining Component and Capabilities, Data Mining Strategy, Benefit of Data Mining Program in Banking
Views: 33 aircc journal
Introduction to Data Mining  (3/3)
 
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http://www.creativecommit.com. This video gives a brief demo of the various data mining techniques. The demo mainly uses SQL server 2008, BIDS 2008 and Excel for data mining
Views: 14580 creativecommIT
What is Spatial Data - An Introduction to Spatial Data and its Applications
 
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Learn more advanced front-end and full-stack development at: https://www.fullstackacademy.com Spatial Data, also referred to as geospatial data, is the information that identifies the geographic location of physical objects on Earth. It’s data that can be mapped, as it is stored as coordinates and topology. In this video, we introduce the concept of Spatial Data and break down the fundamentals of interacting with Spatial Data using common development tools. We then explore how these basics can be expanded upon in modern applications to assist in daily tasks, perform detailed analyses, or create interactive user experiences. Watch this video to learn: - What is Spatial Data - How and when to use Spatial Data - Spatial Data Examples and real-world applications
Views: 4540 Fullstack Academy
Govt Uses of Data Mining
 
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Suffolk Univ Project
Views: 246 joeldsilva
Analysis of Chicago City Crime Data Using Data Mining CS 5593 OU
 
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Application for the project of Analysis of Chicago City Crime Data using Data mining for The University of Oklahoma class CS - 5593 0:00 Clustering application 5:37 Classification Application Members of the group: Cristian Paez Pravallika Uppuganti Ryan Kiel
Views: 983 Cristian Paez
Advanced Data Mining with Weka (3.6: Application: Functional MRI Neuroimaging data)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 6: Application: Functional MRI Neuroimaging data http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/8yXNiM https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 1303 WekaMOOC
What is Clustering?, What is Data Mining?, Data Mining Applications
 
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BigData COE offers interactive online classes and Provide Live Case Studies to help you understand the subject by the certified professionals
Views: 73 Bigdata Coe
Introduction to Data Mining  (2/3)
 
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http://www.creativecommit.com. This video gives a brief demo of the various data mining techniques. The demo mainly uses SQL server 2008, BIDS 2008 and Excel for data mining
Views: 26765 creativecommIT
What is machine learning and how to learn it ?
 
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http://www.LearnCodeOnline.in Machine learning is just to give trained data to a program and get better result for complex problems. It is very close to data mining. While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with: The heavily hyped, self-driving Google car? The essence of machine learning. Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life. Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation. Fraud detection? One of the more obvious, important uses in our world today. fb: https://www.facebook.com/HiteshChoudharyPage homepage: http://www.hiteshChoudhary.com
Views: 659629 Hitesh Choudhary
What is DATA STREAM MINING? What does DATA STREAM MINING mean? DATA STREAM MINING meaning
 
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What is DATA STREAM MINING? What does V mean? DATA STREAM MINING meaning - DATA STREAM MINING definition - DATA STREAM MINING 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 Data Stream Mining is the process of extracting knowledge structures from continuous, rapid data records. A data stream is an ordered sequence of instances that in many applications of data stream mining can be read only once or a small number of times using limited computing and storage capabilities. In many data stream mining applications, the goal is to predict the class or value of new instances in the data stream given some knowledge about the class membership or values of previous instances in the data stream. Machine learning techniques can be used to learn this prediction task from labeled examples in an automated fashion. Often, concepts from the field of incremental learning are applied to cope with structural changes, on-line learning and real-time demands. In many applications, especially operating within non-stationary environments, the distribution underlying the instances or the rules underlying their labeling may change over time, i.e. the goal of the prediction, the class to be predicted or the target value to be predicted, may change over time. This problem is referred to as concept drift. Examples of data streams include computer network traffic, phone conversations, ATM transactions, web searches, and sensor data. Data stream mining can be considered a subfield of data mining, machine learning, and knowledge discovery.
Views: 533 The Audiopedia
What is DATA MINING? What does DATA MINING mean? DATA MINING meaning, definition & explanation
 
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What is DATA MINING? What does DATA MINING mean? DATA MINING meaning - DATA MINING definition - DATA MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Data mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons. Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate. The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps. The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.
Views: 5682 The Audiopedia
Mining Web Data for Public Health
 
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Recent years have seen the adoption of new Web data sources in a wide range of health areas. Of all areas, public health applications in behavioral medicine have the most potential to change how we conduct research, opening up exciting new opportunities. Fundamentally, behavioral medicine requires understanding how people make health decisions: what influences their decision, how they weigh information, and how social connections impact decisions. Web data sources provide new opportunities for studying these questions. Answering these questions often requires new data mining methods. In this talk, I will present multi-dimensional topic models of text which jointly capture topic and other aspects of text. We describe Factorial Latent Dirichlet Allocation, a multi-dimensional model in which a document is influenced by K different factors, and each word token depends on a K-dimensional vector of latent variables. I will demonstrate the advantages of this model in the application of mining drug experiences from web forums.
Views: 118 Microsoft Research
Disease Prediction System using Data Mining
 
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Disease Prediction System using Data Mining 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 The successful application of data mining in highly visible fields like e-business, commerce and trade has led to its application in other industries. The medical environment is still information rich but knowledge weak. There is a wealth of data possible within the medical systems. However, there is a lack of powerful analysis tools to identify hidden relationships and trends in data. Heart disease is a term that assigns to a large number of heath care conditions related to heart. These medical conditions describe the unexpected health conditions that directly control the heart and all its parts. Medical data mining techniques like association rule mining, classification, clustering is implemented to analyze the different kinds of heart based problems. Classification is an important problem in data mining. Given a database contain collection of records, each with a single class label, a classifier performs a brief and clear definition for each class that can be used to classify successive records. A number of popular classifiers construct decision trees to generate class models. The data classification is based on MAFIA algorithms which result in accuracy, the data is estimated using entropy based cross validations and partition techniques and the results are compared. The heart disease database is clustered using the K-means clustering algorithm, which will remove the data applicable to heart attack from the database.

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