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How to process text files with RapidMiner
 
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In this video I process transcriptions from Hugo Chavez's TV programme "Alo Presidente" to find patterns in his speech. Watching this video you will learn how to: -Download several documents at once from a webpage using a Firefox plugin. - Batch convert pdf files to text using a very simple script and a java application. - Process documents with Rapid Miner using their association rules feature to find patterns in them.
Views: 34765 Alba Madriz
Download full web pages with Data Miner
 
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This video will quickly cover the process of downloading full web pages with the Data Miner tool. This is will capture text, images and any other page elements, without even needing a recipe for the site. Learn more: https://data-miner.io/features/download-pages Download Data Miner from the Chrome store: https://chrome.google.com/webstore/detail/data-scraper/nndknepjnldbdbepjfgmncbggmopgden Also, try our new tool Recipe Creator! Create your own custom recipes in minutes. Learn More here: data-miner.io/rc
Views: 7102 Data Miner
Processing Text In RapidMiner
 
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Part 2 of 5. This video discusses processing text in RapidMiner, including tokenizing, stemming, stopwords, and n-grams.
Views: 66422 el chief
03 Visualizing Data in RapidMiner Studio
 
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Download the sample tutorial files at http://static.rapidminer.com/education/getting_started/Follow-along-Files.zip
Views: 9265 RapidMiner, Inc.
11 Connecting to Databases
 
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Download the sample tutorial files at http://static.rapidminer.com/education/getting_started/Follow-along-Files.zip
Views: 3419 RapidMiner, Inc.
Firequark : quick html screen scraping
 
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An extension to Firebug to automatically generate CSS Selectors. Homepage : http://quarkruby.com/2007/9/5/firequark-quick-html-screen- scraping
Views: 350 nakula3
Introduction to RapidMiner 8
 
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For all users starting out on RapidMiner 8. This is an overview video that includes the use of AutoModel!
Views: 195 NeuralMarketTrends
Weka Text Classification for First Time & Beginner Users
 
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59-minute beginner-friendly tutorial on text classification in WEKA; all text changes to numbers and categories after 1-2, so 3-5 relate to many other data analysis (not specifically text classification) using WEKA. 5 main sections: 0:00 Introduction (5 minutes) 5:06 TextToDirectoryLoader (3 minutes) 8:12 StringToWordVector (19 minutes) 27:37 AttributeSelect (10 minutes) 37:37 Cost Sensitivity and Class Imbalance (8 minutes) 45:45 Classifiers (14 minutes) 59:07 Conclusion (20 seconds) Some notable sub-sections: - Section 1 - 5:49 TextDirectoryLoader Command (1 minute) - Section 2 - 6:44 ARFF File Syntax (1 minute 30 seconds) 8:10 Vectorizing Documents (2 minutes) 10:15 WordsToKeep setting/Word Presence (1 minute 10 seconds) 11:26 OutputWordCount setting/Word Frequency (25 seconds) 11:51 DoNotOperateOnAPerClassBasis setting (40 seconds) 12:34 IDFTransform and TFTransform settings/TF-IDF score (1 minute 30 seconds) 14:09 NormalizeDocLength setting (1 minute 17 seconds) 15:46 Stemmer setting/Lemmatization (1 minute 10 seconds) 16:56 Stopwords setting/Custom Stopwords File (1 minute 54 seconds) 18:50 Tokenizer setting/NGram Tokenizer/Bigrams/Trigrams/Alphabetical Tokenizer (2 minutes 35 seconds) 21:25 MinTermFreq setting (20 seconds) 21:45 PeriodicPruning setting (40 seconds) 22:25 AttributeNamePrefix setting (16 seconds) 22:42 LowerCaseTokens setting (1 minute 2 seconds) 23:45 AttributeIndices setting (2 minutes 4 seconds) - Section 3 - 28:07 AttributeSelect for reducing dataset to improve classifier performance/InfoGainEval evaluator/Ranker search (7 minutes) - Section 4 - 38:32 CostSensitiveClassifer/Adding cost effectiveness to base classifier (2 minutes 20 seconds) 42:17 Resample filter/Example of undersampling majority class (1 minute 10 seconds) 43:27 SMOTE filter/Example of oversampling the minority class (1 minute) - Section 5 - 45:34 Training vs. Testing Datasets (1 minute 32 seconds) 47:07 Naive Bayes Classifier (1 minute 57 seconds) 49:04 Multinomial Naive Bayes Classifier (10 seconds) 49:33 K Nearest Neighbor Classifier (1 minute 34 seconds) 51:17 J48 (Decision Tree) Classifier (2 minutes 32 seconds) 53:50 Random Forest Classifier (1 minute 39 seconds) 55:55 SMO (Support Vector Machine) Classifier (1 minute 38 seconds) 57:35 Supervised vs Semi-Supervised vs Unsupervised Learning/Clustering (1 minute 20 seconds) Classifiers introduces you to six (but not all) of WEKA's popular classifiers for text mining; 1) Naive Bayes, 2) Multinomial Naive Bayes, 3) K Nearest Neighbor, 4) J48, 5) Random Forest and 6) SMO. Each StringToWordVector setting is shown, e.g. tokenizer, outputWordCounts, normalizeDocLength, TF-IDF, stopwords, stemmer, etc. These are ways of representing documents as document vectors. Automatically converting 2,000 text files (plain text documents) into an ARFF file with TextDirectoryLoader is shown. Additionally shown is AttributeSelect which is a way of improving classifier performance by reducing the dataset. Cost-Sensitive Classifier is shown which is a way of assigning weights to different types of guesses. Resample and SMOTE are shown as ways of undersampling the majority class and oversampling the majority class. Introductory tips are shared throughout, e.g. distinguishing supervised learning (which is most of data mining) from semi-supervised and unsupervised learning, making identically-formatted training and testing datasets, how to easily subset outliers with the Visualize tab and more... ---------- Update March 24, 2014: Some people asked where to download the movie review data. It is named Polarity_Dataset_v2.0 and shared on Bo Pang's Cornell Ph.D. student page http://www.cs.cornell.edu/People/pabo/movie-review-data/ (Bo Pang is now a Senior Research Scientist at Google)
Views: 131781 Brandon Weinberg
What is Data Miner?
 
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Data Miner is a Chrome extension with the most features to fill all your scraping needs. Watch this quick video to learn what Data Miner can do. See how it can scrape any web page using community generated recipes, automatically click through pages and also automatically fill forms. Download Data Miner from the Chrome store: https://chrome.google.com/webstore/detail/data-scraper/nndknepjnldbdbepjfgmncbggmopgden Also try our new tool Recipe Creator! Create your own custom recipes in minutes. Learn More here: data-miner.io/rc Happy Scraping!
Views: 10279 Data Miner
NWFE and KNWFE-7
 
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Nonparametric Weighted Feature Extraction (NWFE) Abstract: In this paper, a new nonparametric feature extraction method is proposed for high dimensional multiclass pattern recognition problems. It is based on a nonparametric extension of scatter matrices. There are at least two advantages to using the proposed nonparametric scatter matrices. First, they are generally of full rank. This provides the ability to specify the number of extracted features desired and to reduce the effect of the singularity problem. This is in contrast to parametric discriminant analysis, which usually only can extract L--1 (number of classes minus one) features. In a real situation, this may not be enough. Second, the nonparametric nature of scatter matrices reduces the effects of outliers and works well even for non-normal data sets. The new method provides greater weight to samples near the expected decision boundary. This tends to provide for increased classification accuracy. Index Terms—Dimensionality reduction, discriminant analysis, nonparametric feature extraction. Download Full Paper Kernel Nonparametric Weighted Feature Extraction (KNWFE) Abstract: In the recent years, many studies show that kernel methods are computationally efficient, robust, and stable for pattern analysis. Many kernel-based classifiers were designed and applied to classify remote-sensed data and some results show that kernel-based classifiers have satisfying performances. Many researches about hyperspectral image classification also show that nonparametric weighted feature extraction (NWFE) is a powerful tool for extracting hyperspectral image features. But NWFE is still based on linear transformation. In this paper, kernel method is applied to extend NWFE to kernel-based NWFE (KNWFE). The new KNWFE possesses the advantages of both linear and nonlinear transformation and the experimental results show that KNWFE outperforms NWFE, DBFE, ICA , KPCA, and GDA. Download Full Paper External Link: http://kbc.ntcu.edu.tw/
Views: 51 李政軒
"Local to Global" Expo 2015 - RapidMiner - company pitch
 
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Rapidminer empowers enterprises to easily mashup data, create predictive models, and operationalize predictive analytics within any business process.
Views: 58 PwCsAccelerator
Programmtipp: LicenseCrawler
 
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Hier geht es um das Programm LicenseCrawler. Mit dem Programm kann man Seriennummern und den Windows Product Key, die in der Registry sind, herausholen
Views: 1590 Sidney Kuyateh
how to install LIBSVM on matlab
 
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how to install LIBSVM on matlab and visual c++ express compiler
Views: 53007 Merouane Amraoui
ClaroCapture - Capturing Text
 
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ClaroCapture Help Video - Learn how to capture text
Views: 1533 Claro Software
Image Management in IBM Watson Studio (previously called IBM Data Science Experience Local)
 
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This video shows how you can augment the IBM-provided images In Data Science Experience Local to add your own set of packages and libraries. You can then upload these images so that your DSX Local users can use them to create assets such as Jupyter notebooks, Zeppelin notebooks, and RStudio files. Learn more: https://ibm.co/2MEn98S
Views: 618 IBM Analytics
Author gender identification from text
 
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Text is still the most prevalent Internet media type. Examples of this include popular social networking applications such as Twitter, Craigslist, Facebook, etc. Other web applications such as e-mail, blog, chat rooms, etc. are also mostly text based. A question we address in this paper that deals with text based Internet forensics is the following: given a short text document, can we identify if the author is a man or a woman? This question is motivated by recent events where people faked their gender on the Internet. Note that this is different from the authorship attribution problem. In this paper we investigate author gender identification for short length, multi-genre, content-free text, such as the ones found in many Internet applications. Fundamental questions we ask are: do men and women inherently use different classes of language styles? If this is true, what are good linguistic features that indicate gender? Based on research in human psychology, we propose 545 psycho-linguistic and gender-preferential cues along with stylometric features to build the feature space for this identification problem. Note that identifying the correct set of features that indicate gender is an open research problem. Three machine learning algorithms (support vector machine, Bayesian logistic regression and AdaBoost decision tree) are then designed for gender identification based on the proposed features. Extensive experiments on large text corpora (Reuters Corpus Volume 1 newsgroup data and Enron e-mail data) indicate an accuracy up to 85.1% in identifying the gender. Experiments also indicate that function words, word-based features and structural features are significant gender discriminators.
How To Install KNIME Analytics Platform on Windows
 
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This video shows how to install the KNIME Analytics Platform core on Windows, by choosing one of the 4 installation options. - Installation of KNIME Analytics Platform on Linux available at https://youtu.be/wibggQYr4ZA - Installation of KNIME Analytics Platform on Mac available at https://youtu.be/1jvRWryJ220 Next: "How to install Extensions in KNIME Analytics Platform" https://youtu.be/8HMx3mjJXiw "Getting around the KNIME Welcome Page" https://youtu.be/Jib9t6hK6Bg
Views: 15587 KNIMETV
Data Explorer: Interactive Univariate Visual Exploration
 
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You can learn a lot from your data by simply exploring the statistical properties of the different input columns. KNIME Analytics Platform has a dedicated node for the preliminary and generic visual exploration of the data at hand - the Data Explorer node. The Data Explorer node provides an interactive view for univariate exploration of numerical and nominal features. In this video we would like to give you an idea of how powerful this Data Explorer node can be. The workflow used in this video is available on the public KNIME EXAMPLES server under: 03_Visualization/02_JavaScript/11_Univariate_Visual_Exploration_with_Data_Explorer The Data Explorer (JavaScript) Node is part of the extension KNIME JavaScript Views (Labs). To install a KNIME Extension, follow instructions in this video: https://youtu.be/8HMx3mjJXiw The KNIME workflow in this video contains data from WeatherUnderground.com, from the Austin KATT station, which is released under GPLv2. Data source: https://www.wunderground.com/history/airport/KATT/ Data license: https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html KNIME Analytics Platform is a free, open source data analytics software which can be downloaded at https://www.knime.com/downloads
Views: 956 KNIMETV
How to Tweet by Location
 
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Learn how to tweet by location. Enable the option which allows you to include location in your tweets. Don't forget to check out our site http://howtech.tv/ for more free how-to videos! http://youtube.com/ithowtovids - our feed http://www.facebook.com/howtechtv - join us on facebook https://plus.google.com/103440382717658277879 - our group in Google+ In this tutorial, we will teach you how to tweet by location. Twitter has this option which allows you to include location in your tweets. Step 1 -- Go to twitter settings Follow this step by step guide to learn how to tweet by location. First of all, click on the settings button available on the top right corner of your twitter home page. From the drop down menu, select the settings option. Step 2 -- Add location to tweets As a result the account settings page will open. Scroll down a little and check the option titled "add a location to my tweets". Once you are done, go to the very bottom of the page and click on the save changes button. Step 3 -- Save Account Changes The save account changes pop up will appear n your screen. Over there, enter your twitter password and click on the save changes button in order to apply the changes that you just made. Step 4 -- Choose a location When the settings page reloads, go to the extreme top left corner of the web page and click on the home tab. Once you are there, compose a new tweet. Once you have entered the text that you want to tweet, click on the locations drop down button and select one of the locations available. Once you are done, click on the tweet button. Step 5 -- Expand tweet You'd be notified that the tweet was posted. Go to the tweet that you just posted and click on the expand option. The location that you chose would be displayed here. In this manner, you can include location in tweets.
Using OutWit to capture resume data for uploading to your ATS
 
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A couple of ways to add value to your searching for candidates online using the Firefox extension "OutWit Docs" and "OutWit Hub." Taken from the webinar series: "Untangling the Web: Recruiting with Google, LinkedIn, Twitter and most everything in between."
Views: 4647 Recruitomatic
Web scraping with XPath to locate data ( Octoparse 7.X)
 
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XPath is a must to locate data when scraping web data, this tutorial will show you when and where to modify the XPath of the element, and pick out the information you want. Graphic tutorial please visit: https://www.octoparse.com/tutorial-7/xpath/ To learn how to modify the XPath to locate the next page, you could watch another video:https://youtu.be/57xR_TEuxys
Views: 1354 Octoparse
CMSCC Content System Manager Crawl Content Solutions to news website
 
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CMS CC (Content System Manager Crawl Content Solutions for news website) Drupal CMS, Joomla CMS , WordPress CMS , Redaxscript. You can get hundreds news and post it to your site in some minute. link infomation: http://dmwjsc.com/cmscc/
Views: 103 Long Ngô Hùng
How to use Google Chrome Scraper Plugin
 
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Full tutorial including introduction to plugin , download links and into to XPath is available here : http://blog.saijogeorge.com/use-google-chrome-scraper-plugin-extract-data-websites-coding-experience-required/ This is a quick look at how to use Google Chrome Scraper Plugin
Views: 76083 Saijo George
Deep Learning Lecture 8: Introducing Keras
 
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For my larger Machine Learning course, see https://www.udemy.com/data-science-and-machine-learning-with-python-hands-on/?couponCode=DATASCIENCE15 We'll introduce the Keras Python library that sits on top of Tensorflow, making the construction of deep neural networks a lot easier. We'll then use it on the MNIST handwriting recognition problem and see how much easier and better it is than using Tensorflow directly.
Views: 1227 Sundog Education
Extract Information from LinkedIn
 
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Many people asked “Is there any way to scrape data from public profiles on LinkedIn?"You may want to pull some info from LinkedIn, like people who followed your company, members information of a group. In this video, I’m going to share with you how to scrape data from LinkedIn public profiles. For more information please check out www.octoparse.com.
Views: 34991 Octoparse
SmokeDoc Web Screen Scraping Tool Promo
 
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http://smokedoc.org/ - SmokeDoc is your complete web scraping and data extraction suite which is helping you to extract information from the web sites with higher profits and faster than ever. The extracted data can be converted and saved in any text format. SmokeDoc will safe your time and make the processing of voluminous text documents easier.
Views: 477 seobucks
2. R Statistics Integration part of the "What's new" talk at KNIME UGM 2014
 
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A new video in higher resolution is now available at http://youtu.be/y1hrLJzsPws This video is part of the full recording of the "What's new" talk by Bern Wiswedel (KNIME CTO) and the KNIME developer group at the KNIME User Group Meeting in Zurich on February 12 2014. This video focuses on R Statistics Integration for KNIME 2.8 and 2.9 and is presented by Heiko Hofer. He shows the new powerful R(Interactive) nodes and how to edit, debug, store, and retrieve R scripts within a KNIME workflow. Slides can downloaded from http://www.knime.com/ugm2014 The full recording is available at http://youtu.be/6mmarTp7V-0
Views: 1000 KNIMETV
KNIME Report Creation
 
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Views: 10312 KNIMETV
WEKA API 3/19: Converting CSV to ARFF and ARFF to CSV
 
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To access the code go to the Machine Learning Tutorials Section on the Tutorials page here: http://www.brunel.ac.uk/~csstnns Using WEKA in java
Views: 54516 Noureddin Sadawi
Executing Open Source Code in Machine Learning Pipelines
 
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Executing Open Source Code in Machine Learning Pipelines of SAS Visual Data Mining and Machine Learning http://support.sas.com/software/products/visual-data-mining-machine-learning/index.html Presenter: Radhikha Myneni Radhikha Myneni demonstrates how to execute open source code, specifically Python and R in SAS Visual Data Mining & Machine Learning pipelines. SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®. VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 789 SAS Software
Multi-level extraction
 
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This video shows how to extract data to many tables and maintain the relations among the extracted elements in the output database.
Views: 9840 HeliumScraper
Data Access with KNIME: Sending a GET Request to a REST service
 
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The first step to building a micro-service based architecture is to be able to call a REST service. In this video we explain how to send a GET Request to a REST service from within a KNIME workflow using the GET Request node. Example workflows on how to call a REST service from a KNIME workflow can be found as usual on the KNIME EXAMPLES server in folder 01_Data_Access/05_REST_Web_Services. Other related videos: - Reading a text file https://youtu.be/flaHQw-Qhlg - Reading a table file https://youtu.be/tid1qi2HAOo - Reading an Excel file https://youtu.be/goo5ClHlfT8
Views: 1971 KNIMETV
Vector Express Lesson 4 – Deploy a KNIME ETL workflow
 
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Once designed, ETL workflows are often executed in an automated fashion. This lesson teaches you how to use the KNIME headless batch mode to deploy an Actian Vector Express workflow. Actian Vector Express can be downloaded from here: http://bigdata.actian.com/express If you have any questions, try the Actian Vector Express FAQ here: http://img.en25.com/Web/Actian/%7B0dc75c40-c77f-4e77-a7f9-6550f3dd394f%7D_Vector_Express_FAQs_03132015.pdf If you have comments, get stuck, or just want to chat about your project, join the Actian Vector Community forum here: http://supportservices.actian.com/community
Views: 479 Actian Corporation
Create a Simple Word Cloud in Excel
 
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Check out my Blog: http://exceltraining101.blogspot.com How to create a simple word cloud in Excel. This trick uses a few functions and special paste feature. Feel free to provide a comment or share it with a friend! #exceltips #exceltipsandtricks #exceltutorial #doughexcel #exceltips #exceltipsandtricks #exceltutorial #doughexcel #exceltips #exceltipsandtricks #exceltutorial #doughexcel #exceltips #exceltipsandtricks #exceltutorial #doughexcel
Views: 45402 Doug H
Bishan Kochar : Secure Coding with YUI
 
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In this demo-oriented talk from YUIConf 2012, the Yahoo! web security engineer Bishan presents both common and not so common security issues arising out of unsafe YUI coding. He analyzes real world vulnerable examples and follows with code demos that show the correct way of securing those with YUI. Bishan also talks about good security patterns that can eliminate a number of frontend vulnerabilities that are seen today.
Views: 737 YUI Library
What am I seeing? Cool and Not-So-Cool Data Visualization
 
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Data visualization is everywhere and is key to bring data to life. There are some great, easy-to-use data visualization tools and websites that can entice even those with math phobia to play with data. Still, visualized data need context and interpretation. This session will discuss data visualization tools and suggest mechanisms to assist users in interpreting what they create. Presenter(s): Justin Joque & Kate Saylor, University of Michigan Libraries Presentation slides: https://drive.google.com/file/d/0B2H9zhZqon4UbUMtMXFMN0NHbnc/view?usp=sharing
Views: 223 ICPSR
Entity-Relationship Diagram in StarUML
 
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Datamodellering med entitets-relationsdiagram i StarUML
Views: 68163 Andreas Larsson

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