Search results “Text mining research issues”
Text Mining Problems
I would like to thank Lauren Briggs (Durban, South Africa) and Sean Pethybridge (Surf City, New Jersey) for giving voices to Laura, Saundra and Markus.
Views: 172 Fabio Stella
Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topic-contextaware-computing/
Views: 494 PHD Projects
#FixCopyright:  Copyright & Research - Text & Data Mining (TDM) Explained
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: 3036 FixCopyright
Data Mining and Text Mining with John Elder
Analytics 2014 Conference Keynote Conference John Elder of Elder Research explains the top three challenges of data mining and text mining, and how to solve them. Learn more about Analytics 2014 at http://www.sas.com/analyticsseries/us/
Views: 1143 SAS Software
15 Hot Trending PHD Research Topics in Data Mining 2018
15 Hot Trending Data Mining Research Topics 2018 1. Medical Data Mining 2. Education Data Mining 3. Data Mining with Cloud Computing 4. Efficiency of Data Mining Algorithms 5. Signal Processing 6. Social Media Analytics 7. Data Mining in Medical Science 8. Government Domain 9. Financial Data Analysis 10. Financial Accounting Fraud Detection 11. Customer Analysis 12. Financial Growth Analysis using Data Mining 13. Data Mining and IOT 14. Data Mining for Counter-Terrorism Key Research Application Fields: • Crisp-DM • Oracle Data mining • Web Mining • Open NN • Data Warehousing • Text Mining WHY YOU NEED TO OUTSOURCE TO PhD Assistance: a) Unlimited revisions b) 24/7 Admin Support c) Plagiarism Generate d) Best Possible Turnaround time e) Access to High qualified technical coordinators and expertise f) Support: Skype, Live Chat, Phone, Email Contact us: India: +91 8754446690 UK: +44-1143520021 Email: [email protected] Visit Webpage: https://goo.gl/HwJgqQ Visit Website: http://www.phdassistance.com
Views: 1875 PhD Assistance
Text mining with correspondence analysis
Here is an example of the use of correspondence analysis for textual data. Four methods of multivariate data analysis are descibed by words and compared with correspondence analysis.
Views: 4255 François Husson
What are the benefits of Text and Data Mining? - Monica Ihli
Video recorded at the Workshop On mining Scientific Publications, 19th-23rd June at The University of Toronto, as a part of JCDL 2017 (Joint Conference on Digital Libraries).
Views: 151 OpenMinTeD
Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topic-mobile-networking/
Views: 3688 PHD Projects
Torsten Reimer on the value and benefits of text mining
Interview with Dr Torsten Reimer, JISC Programme Manager on the JISC-funded Analysis of the Value and Benefits of Text Mining and Text Analytics to UK Further and Higher Education. For more details see the full JISC- funded report by Intelligent Digital Options - http://www.jisc.ac.uk/publications/reports/2012/value-and-benefits-of-text-mining.aspx.
Views: 317 InDigONetwork
How does Text Mining Work?
Understand the basics of how text and data mining works and how it is used to help advance science and medicine. To learn what text mining is, view the video "What is Text Mining?" here: https://youtu.be/I3cjbB38Z4A
Views: 11630 Elsevier
FDP on Data Mining - Tools and Research Issues by Dr A V KrishnaPrasad
FDP on Data Mining - Tools and Research Issues by Dr A V KrishnaPrasad
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Views: 565 PHD Projects
research paper topics in data mining
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Text Mining & Libraries: What can we learn from HathiTrust, digital scholars, and the ASHE project?
A discussion of libraries' use of full-text metadata for research including copyright issues, scholars' text mining practices, and lessons to be learned from ASHE (Automatic Subject Heading Extraction), a project that has already used text mining to enhance discovery.
Views: 1161 Harvard University
Victor Henning discusses the value and benefits of text mining to Mendeley
Interview with Dr Victor Henning CEO and Co-founder of Mendeley on the value and benefits of text mining. This includes discussion of new services and business models. For more details see the full JISC- funded report by Intelligent Digital Options - http://www.jisc.ac.uk/publications/reports/2012/value-and-benefits-of-text-mining.aspx.
Views: 243 InDigONetwork
find relevant notes at-https://viden.io/
Views: 6810 LearnEveryone
John McNaught, NaCTeM, discusses text mining
Interview with John McNaught of NaCTeM on the Value and Benefits of Text Mining to further and higher education. For more details see the full JISC- funded report by Intelligent Digital Options - http://www.jisc.ac.uk/publications/reports/2012/value-and-benefits-of-text-mining.aspx.
Views: 176 InDigONetwork
Data Mining & Business Intelligence | Tutorial #3 | Issues in Data Mining
This video addresses the issues which are there involved in Data Mining system. Watch now !
Views: 835 Ranji Raj
GTU Research Week Workshop by Dr. S Nickolas : Data Mining & its Applications
GTU Research Week 2015 Workshop conducted by Dr. S Nickolas on Data Mining & its Applications at Gujarat Technological University, Chandkheda, Ahmedabad
Challenges and Issues in various types of Data Mining
Challenges and Issues in various types of Data Mining
Data mining issues and challenges. By game fan
Describe what is data mining and its issues step by step Game fan Gamefan
Views: 1544 K game fan
Text Mining YouTube Comments
Brooke Fortson interviews Murali Pagolu from SAS who talks about using SAS Text Miner to analyze YouTube comments, gaining insight into what customers are saying about a company's products. To learn more about Analytics 2011, visit http://www.sas.com/analyticsseries .
Views: 1706 SAS Software
Decision Tree with Solved Example in English | DWM | ML | BDA
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: 132561 Last moment tuitions
Barbara Plank | Keynote - Natural Language Processing: Challenges and Next Frontiers
Barbara Plank is tenured Assistant Professor in Natural Language Processing at the University of Groningen, The Netherlands. Her research focuses on cross-domain and cross-language NLP. She is interested in robust language technology, learning under sample selection bias (domain adaptation, transfer learning), annotation bias (embracing annotator disagreements in learning), and generally, semi-supervised and weakly-supervised machine learning for a variety of NLP tasks and applications, including syntactic processing, opinion mining, information and relation extraction and personality prediction. Natural Language Processing: Challenges and Next Frontiers Despite many advances of Natural Language Processing (NLP) in recent years, largely due to the advent of deep learning approaches, there are still many challenges ahead to build successful NLP models. In this talk I will outline what makes NLP so challenging. Besides ambiguity, one major challenges is variability. In NLP, we typically deal with data from a variety of sources, like data from different domains, languages and media, while assuming that our models work well on a range of tasks, from classification to structured prediction. Data variability is an issue that affects all NLP models. I will then delineate one possible way to go about it, by combining recent success in deep multi-task learning with fortuitous data sources, which allows learning from distinct views and distinct sources. This will be one step towards one of the next frontiers: learning under limited (or absence) of annotated resources, for a variety of NLP tasks. Link to Q&A: https://youtu.be/JtiCdsESuT0 www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 2312 PyData
Current trends in Data Mining..
Topic described here are: Multimedia datamining Ubiquitous datamining Distributed datamining Spatial datamining Time series datamining Text mining Video mining Image mining Audio mining multimedia issues Submitted by: A. Vaishnavi II Msc cs A 175214141
Views: 50 vaishu raj
Data Mining Marketing Research ChannelAide
http://www.channelaide.com/ marketing research done for your online selling
Views: 194 Mike Gerts
Semantic Web Mining
Semantic Web Mining by Dr. S Yasodha
Views: 382 Krish eClasses
Quantitative Text Analysis for Social Scientists  A talk by Nicole Rae Baerg
Nicole Rae Baerg, lecturer at the Department of Government at the University of Essex, discusses qualitative text analysis in this SAGETalks webinar. Text analysis has a long history in the social sciences and has been commonly used to analyze media coverage. Historically, it involved the human coding of text and this has inherent issues. The digital age has made huge amounts of data available for analysis in the form of newspapers, blogs, social media feeds, government documents, the list goes on! As the technology to automate the analysis and coding of texts has become more available we are able to go beyond this and treat text as quantifiable data. Watch this webinar to learn about the role that quantitative text analysis plays for social scientists when working with such vast amounts of data, as well hearing about ‘QTA in action’ and how social scientists are using text analysis for their research.
Views: 393 SAGE
PhD research topic in Image Mining
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Views: 777 PHD Projects
The Logic of Data Mining in Social Research
This video is a brief introduction for undergraduates to the logic (not the nitty-gritty details) of data mining in social science research. Four orienting tips for getting started and placing data mining in the broader context of social research are included.
Views: 284 James Cook
Social media data mining for counter-terrorism | Wassim Zoghlami | TEDxMünster
Using public social media data from twitter and Facebook, actions and announcements of terrorists – in this case ISIS – can be monitored and even be predicted. With his project #DataShield Wassim shares his idea of having a tool to identify oncoming threats and attacks in order to protect people and to induce preventive actions. Wassim Zoghlami is a Tunisian Computer Engineering Senior focussing on Business Intelligence and ERP with a passion for data science, software life cycle and UX. Wassim is also an award winning serial entrepreneur working on startups in healthcare and prevention solutions in both Tunisia and The United States. During the past years Wassim has been working on different projects and campaigns about using data driven technology to help people working to uphold human rights and to promote civic engagement and culture across Tunisia and the MENA region. He is also the co-founder of the Tunisian Center for Civic Engagement, a strong advocate for open access to research, open data and open educational resources and one of the Global Shapers in Tunis. At TEDxMünster Wassim will talk about public social media data mining for counter-terrorism and his project idea DataShield. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 1729 TEDx Talks
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: 99889 LearnEveryone
Text Analytics and Text Mining Explained by OdinText
Text Analytics Explained. Anderson Analytictics, developers of Next Generation Text Analytics software platform OdinText explain Text Analytics and the power of text mining, as well as the difference between first generation text analytics software from IBM SPSS, SAS Text, Attensity and Clarabridge compared to the OdinText Next Generation Text Analytics approach to text and data mining. http://www.OdinText.com
Views: 25333 OdinText
Web Mining and Social Analytics - Dr. Jaideep Srivastava
With the mass adoption of the Internet in our daily lives, and the ability to capture high resolution data on its use, we are at the threshold of a fundamental shift not only in our understanding of the social and behavioral sciences (i.e. psychology, sociology, and economics), but also the ways in which we study them. Massively Multiplayer Online Games (MMOGs) and Virtual Worlds (VWs) have become increasingly popular and have communities comprising tens of millions. They serve as unprecedented tools to theorize and empirically model the social and behavioral dynamics of individuals, groups, and networks within large communities. The preceding observation has led to a number of multi-disciplinary projects, involving teams of behavioral scientists and computational scientists, working together to develop novel methods and tools to explore the current limits of behavioral sciences. This talk consists of four parts. First, we describe findings from the Virtual World Exploratorium; a multi-institutional, multi-disciplinary project which uses data from commercial MMOGs and VWs to study many fields of social science, including sociology, social psychology, organization theory, group dynamics, macro-economics, etc. Results from investigations into various behavioral sciences will be presented. Second, we provide a survey of new approaches for behavioral informatics that are being developed by multi-disciplinary teams, and their successes. We will also introduce novel tools and techniques that are being used and/or developed as part of this research. Third, we will discuss some novel applications that are not yet there, but are just around the corner, and their associated research issues. Finally, we present commercial applications of Social Analytics research, based on our experiences with a start-up company that we've created.
Views: 369 MOTC QA
Sentimental Analysis in R
Sentiment analysis or opinion mining is the computational study of people’s opinions, sentiments, attitudes, and emotions expressed in written language. Also it refers to the task of natural language processing to determine whether a piece of text contains some subjective information and what subjective information it expresses, i.e., whether the attitude behind this text is positive, negative or neutral. Understanding the opinions behind user-generated content automatically is of great help for commercial and political use, among others. The task can be conducted on different levels, classifying the polarity of words or sentences. It is one of the most active research areas in natural language processing and text mining in recent years. Its popularity is mainly due to two reasons. First, it has a wide range of applications because opinions are central to almost all human activities and are key influencers of our behaviors. Whenever we need to make a decision, we want to hear others’ opinions. Second, it presents many challenging research problems, which had never been attempted before the year 2000. Part of the reason for the lack of study before was that there was little opinionated text in digital forms. It is thus no surprise that the inception and the rapid growth of the field coincide with those of the social media on the Web. In fact, the research has also spread outside of computer science to management sciences and social sciences due to its importance to business and society as a whole.
Views: 3932 Mavericks 045_049_078
Data Mining Research Topics | Data Mining Research Project Topics
Contact Best Matlab Simulations Projects http://matlabsimulations.com/
An SMS Text Classification System for UNICEF Uganda
Speaker: Rick Lawrence, Senior Manager, Machine Learning & Decision Analytics at IBM Research U-report is an open-source SMS platform operated by UNICEF Uganda, designed to give community members a voice on issues that impact them. Data received by the system are either SMS responses to a poll conducted by UNICEF or unsolicited reports of problems occurring anywhere within Uganda. There are currently 300,000 U-report participants, and they can send up to 10,000 unsolicited text messages a week. The objective of the program in Uganda is to understand the data in real-time, and have issues addressed by the appropriate department in UNICEF in a timely manner. This talk describes an automated message-understanding and routing system deployed by IBM and UNICEF in Uganda. We discuss a dual-supervision machine learning approach to leverage human-generated labels on both features and text examples, and conclude with a discussion of the societal impact that U-report is driving in Uganda.
Views: 296 IBM Research
Decision Tree Tutorial in 7 minutes with Decision Tree Analysis & Decision Tree Example (Basic)
Clicked here http://www.MBAbullshit.com/ and OMG wow! I'm SHOCKED how easy.. No wonder others goin crazy sharing this??? Share it with your other friends too! Fun MBAbullshit.com is filled with easy quick video tutorial reviews on topics for MBA, BBA, and business college students on lots of topics from Finance or Financial Management, Quantitative Analysis, Managerial Economics, Strategic Management, Accounting, and many others. Cut through the bullshit to understand MBA!(Coming soon!) http://www.youtube.com/watch?v=a5yWr1hr6QY
Views: 506029 MBAbullshitDotCom
SHOW TELL  2015 10 02
TOPICS COVERED: * Text Mining * SQL Lag Function * Bayesian Inference * SparkR * RStudio Markdown Function * Business Issues related to Analytics
Lecture 46 — Dimensionality Reduction - Introduction | Stanford University
. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Interview with Anthony Breitzman (Rowan University, United States)
My research broadly falls into Data Mining, Text Mining, Mathematical Modeling, Algorithm development. Most of my studies can be described as Science and Technology Policy findings that have been informed by mining large data sets. My current research is focusing on using Text Mining techniques on social media (e.g. Twitter) to further inform these studies. Anthony Breitzman Sr. is an Assistant Professor of Computer science and Data Analytics at Rowan University as well as a co-founding partner of 1790 Analytics. He has consulted extensively with Fortune 500 companies, helping them to develop and manage their intellectual property portfolios more effectively. He has also worked for many different government agencies, helping them assess a wide variety of science and technology policy issues. Anthony is recognized worldwide as a data mining expert and respected thought leader in IP analytics. He has published numerous scholarly articles on mathematics, text mining, intellectual property management, technology assessment and research evaluation. He is invited to speak at conferences worldwide, and is also the inventor of a ground-breaking patent linking patent portfolio strength and stock market performance. Prior to co-founding 1790 Analytics, he was Chief Technology Officer at CHI Research. He holds a B.S. in Mathematics from Richard Stockton University; an M.A. in Mathematics from Temple University; and an M.S. and Ph.D. in Computer Science from Drexel University. This video was recorded at FTC 2017 - http://saiconference.com/FTC
Views: 59 SAIConference
An Introduction to Temporal Databases
Check out http://www.pgconf.us/2015/event/83/ for the full talk details. In the past manipulating temporal data was rather ad hoc and in the form of simple solutions. Today organizations strongly feel the need to support temporal data in a coherent way. Consequently, there is an increasing interest in temporal data and major database vendors recently provide tools for storing and manipulating temporal data. However, these tools are far from being complete in addressing the main issues in handling temporal data. The presentation uses the relational data model in addressing the subtle issues in managing temporal data: comparing database states at two different time points, capturing the periods for concurrent events and accessing to times beyond these periods, sequential semantics, handling multi-valued attributes, temporal grouping and coalescing, temporal integrity constraints, rolling the database to a past state and restructuring temporal data, etc. It also lays the foundation in managing temporal data in NoSQL databases as well. Having ranges as a data type PostgresSQL has a solid base in implementing a temporal database that can address many of these issues successfully. About the Speaker Abdullah Uz Tansel is professor of Computer Information Systems at the Zicklin School of Business at Baruch College and Computer Science PhD program at the Graduate Center. His research interests are database management systems, temporal databases, data mining, and semantic web. Dr. Tansel published many articles in the conferences and journals of ACM and IEEE. Dr. Tansel has a pending patent application on semantic web. Currently, he is researching temporality in RDF and OWL, which are semantic web languages. Dr. Tansel served in program committees of many conferences and headed the editorial board that published the first book on temporal databases in 1993. He is also one the editors of the forth coming book titled Recommendation and Search in Social Networks to be published by Springer. He received BS, MS and PhD degrees from the Middle East Technical University, Ankara Turkey. He also completed his MBA degree in the University of Southern California. Dr. Tansel is a member of ACM and IEEE Computer Society.
Views: 4313 Postgres Conference
Views: 18367 Data Mining - IITKGP
Multilingual Text Mining: Lost in Translation, Found in Native Language Mining - Rohini Srihari
There has been a meteoric rise in the amount of multilingual content on the web. This is primarily due to social media sites such as Facebook, and Twitter, as well as blogs, discussion forums, and reader responses to articles on traditional news sites. Language usage statistics indicate that Chinese is a very close second to English, and could overtake it to become the dominant language on the web. It is also interesting to see the explosive growth in languages such as Arabic. The availability of this content warrants a discussion on how such information can be effectively utilized. Such data can be mined for many purposes including business-related competitive insight, e-commerce, as well as citizen response to current issues. This talk will begin with motivations for multilingual text mining, including commercial and societal applications, digital humanities applications such as semi-automated curation of online discussion forums, and lastly, government applications, where the value proposition (benefits, costs and value) is different, but equally compelling. There are several issues to be touched upon, beginning with the need for processing native language, as opposed to using machine translated text. In tasks such as sentiment or behaviour analysis, it can certainly be argued that a lot is lost in translation, since these depend on subtle nuances in language usage. On the other hand, processing native language is challenging, since it requires a multitude of linguistic resources such as lexicons, grammars, translation dictionaries, and annotated data. This is especially true for "resourceMpoor languages" such as Urdu, and Somali, languages spoken in parts of the world where there is considerable focus nowadays. The availability of content such as multilingual Wikipedia provides an opportunity to automatically generate needed resources, and explore alternate techniques for language processing. The rise of multilingual social media also leads to interesting developments such as code mixing, and code switching giving birth to "new" languages such as Hinglish, Urdish and Spanglish! This phenomena exhibits both pros and cons, in addition to posing difficult challenges to automatic natural language processing. But there is also an opportunity to use crowd-sourcing to preserve languages and dialects that are gradually becoming extinct. It is worthwhile to explore frameworks for facilitating such efforts, which are currently very ad hoc. In summary, the availability of multilingual data provides new opportunities in a variety of applications, and effective mining could lead to better cross-cultural communication. Questions Addressed (i) Motivation for mining multilingual text. (ii) The need for processing native language (vs. machine translated text). (iii) Multilingual Social Media: challenges and opportunities, e.g., preserving languages and dialects.
Stream Data Mining: A Big Data Perspective
Author: Latifur Khan, Department of Computer Science, Erik Jonsson School of Engineering & Computer Science, The University of Texas at Dallas Abstract: Data streams are continuous flows of data. Examples of data streams include network traffic, sensor data, call center records and so on. Data streams demonstrate several unique properties that together conform to the characteristics of big data (i.e., volume, velocity, variety and veracity) and add challenges to data stream mining. In this talk we will present an organized picture on how to handle various data mining techniques in data streams. Most existing data stream classification techniques ignore one important aspect of stream data: arrival of a novel class. We address this issue and propose a data stream classification technique that integrates a novel class detection mechanism into traditional classifiers, enabling automatic detection of novel classes before the true labels of the novel class instances arrive. Novel class detection problem becomes more challenging in the presence of concept-drift, when the underlying data distributions evolve in streams. In this talk we will show how to make fast and correct classification decisions under this constraint with limited labeled training data and apply them to real benchmark data. In addition, we will present a number of stream classification applications such as adaptive malicious code detection, website fingerprinting, evolving insider threat detection and textual stream classification. This research was funded in part by NSF, NASA, Air Force Office of Scientific Research (AFOSR) and Raytheon. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 813 KDD2016 video
averbis healthcare analytics - cloud4health english (en)
More Efficient use of Medical Raw Data in Healhcare & Pharmaceutical Research and Branches Averbis GmbH has been involved as a solution provider in the field Healhcare Analytics & Pharmaceutical Research crucial to the BMWi-funded project. cloud4health taps into large medical raw data inventories for the data protection-friendly evaluation of various issues from the areas of Research, Development and Health Economy. The approach combines text analysis and data warehouse technologies and can be made available either privately or in the public cloud, depending on the need. Altogether three application scenarios will be implemented: the extraction and evaluation of information from anonymized patient data through the operative treatment of hip joints, the development of processes for the automated plausibility and profitability checks of medical treatments, as well as the early identification of undesired side effects of newly introduced medications with the help of automated processes. About Averbis: Averbis stands for cutting edge technology in text mining and text analysis. We support companies worldwide in enhancing their competitive position. Averbis offers solutions for effective searchability, content-related structuring and evaluation of complex information inventories. We analyze unstructured and structured data, e.g. social media data, news, web resources, reports, patents, company-internal data, emails and research literature. Thus, we help you access and evaluate knowledge sources and automate information processes. By integrating in your business processes, we contribute to lowering your costs, boosting productivity, founded decision-making and better prognoses. Methodically, linguistic and semantic, statistical and hybrid search analysis processes are our core competencies. Our excellent team is constantly working on new innovations to always give our customers from the pharmaceutical and automotive industries and the investment, finance and health services a decisive competitive edge. Averbis was founded in 2007 in Freiburg im Breisgau, Germany.
Views: 105 Averbis GmbH
Data Mining Paper Review
Recorded with http://screencast-o-matic.com
Views: 91 venu gopal valeti
Ursula Kelly discusses the value of text mining
Interview with Ursula Kelly, co-author of the JISC-funded Analysis of the value and benefits of text mining to UK Further and Higher Education. For more details see the full JISC- funded report by Intelligent Digital Options - http://www.jisc.ac.uk/publications/reports/2012/value-and-benefits-of-text-mining.aspx.
Views: 102 InDigONetwork
Julie Lavoie | How Soon is Now: extracting publication dates with machine learning
PyData SF 2016 Scraping New York Times articles for publication dates is easy, scraping 10 000 different sites is hard. Beyond page-specific scraping, how do you build a parser than can extract the publication date of (almost) any news article online, no matter what the site is? We implemented a research paper in machine learning to solve this problem, and talk about challenges we faced. Scraping New York Times articles for publication dates is easy, scraping 10 000 different sites is hard. Beyond page-specific scraping, how do you build a parser than can extract the publication date of (almost) any news article online, no matter what the site is? We implemented a research paper in machine learning to solve this problem, and talk about the challenges we faced. We’ll cover when to use machine learning vs. humans or heuristics for data extraction, the different steps of how to phrase the problem in terms of machine learning, including feature selection on HTML documents, and issues that arise when turning research into production code. Data scientists and developers will leave knowing how to extract information from the web using new and more sophisticated techniques than simply writing a scraper.
Views: 880 PyData