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PHD RESEARCH TOPIC IN TEXT MINING
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topic-contextaware-computing/
Views: 423 PHD Projects
Text Mining Problems
 
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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: 143 Fabio Stella
#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: 2925 FixCopyright
Data Mining and Text Mining with John Elder
 
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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: 1119 SAS Software
PHD RESEARCH TOPIC IN DATA MINING
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topic-mobile-networking/
Views: 3292 PHD Projects
Text Mining YouTube Comments
 
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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: 1696 SAS Software
INTRODUCTION TO TEXT MINING IN HINDI
 
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find relevant notes at-https://viden.io/
Views: 6254 LearnEveryone
research paper topics in data mining
 
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Visit Our Website: https://goo.gl/TIo1T2?58204
15 Hot Trending PHD Research Topics in Data Mining 2018
 
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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: 901 PhD Assistance
Challenges and Issues in various types of Data Mining
 
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Challenges and Issues in various types of Data Mining
text mining, web mining and sentiment analysis
 
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text mining, web mining
Views: 1346 Kakoli Bandyopadhyay
Text/Data Mining Webinar: Supporting Researcher Needs
 
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This webinar explored the complex needs and interests of scholars engaged in text and data mining (TDM), and how librarians can meet those needs. What kinds of knowledge and skills are necessary to effectively support this rapidly growing type of research? How can researchers, eager to gain access to large bodies of primary text and data, benefit from library perspectives on the rights of content providers, available tools, and appropriate methodologies? Speakers: Robert Scott, head of the Digital Humanities Center at Columbia University Libraries Kalev Leetaru, currently the Yahoo! Fellow in Residence of International Values, Communications Technology & the Global Internet at the Institute for the Study of Diplomacy, Georgetown University Robert Scott: 4:48 Kalev Leetaru: 27:35 Q&A: 49:50 CRL News: 1:04:32
Views: 307 CRLdotEDU
John McNaught, NaCTeM, discusses text mining
 
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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
PhD research topic in Image Mining
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topic-mobile-cloud-computing/
Views: 730 PHD Projects
FDP on Data Mining - Tools and Research Issues by Dr A V KrishnaPrasad
 
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FDP on Data Mining - Tools and Research Issues by Dr A V KrishnaPrasad
Text Mining & Libraries: What can we learn from HathiTrust, digital scholars, and the ASHE project?
 
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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: 1157 Harvard University
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: 38070 Well Academy
How does Text Mining Work?
 
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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: 10804 Elsevier
Mining Data to Ensure Student Success at Purdue
 
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Purdue University, a major research university located in Lafayette, Indiana known for discoveries in science, technology, engineering and more. Purdue University has become a leader in using data and data science to help students increase student success rates, flag issues, and improve teacher effectiveness. With the help of Pivotal Big Data Suite, data mining techniques, and predictive analytics, the University can give students and teachers an early warning system in situations where students might have challenges.
Data Mining Trends and Research Frontiers - Kelompok Bo Cuan Gpp
 
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Video Presentasi Data Mining Trends and Research Frontiers Kelompok Bo Cuan Gpp
Views: 570 Ria Liuswani
Social media data mining for counter-terrorism | Wassim Zoghlami | TEDxMünster
 
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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: 1614 TEDx Talks
Sentimental Analysis in R
 
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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: 3805 Mavericks 045_049_078
Web Mining and Social Analytics - Dr. Jaideep Srivastava
 
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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: 361 MOTC QA
The Logic of Data Mining in Social Research
 
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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: 259 James Cook
Deep Learning for Text Processing
 
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Deep learning has enjoyed tremendous success in recent years in speech and visual object recognition, as well as in language processing (although to somewhat less extent). The focus of this session is on deep learning approaches to problems in language or text processing, with particular emphasis on important applications with vital significance to Microsoft. First, we will have both academic and Microsoft Research experts provide a tutorial on the latest deep learning technology, presenting both theoretical and practical perspectives on common methods of deep neural networks and recurrent, recursive, stacking, and convolutional networks. We will highlight special challenges faced by language/text processing, and elaborate on how new deep learning technologies are poised to fundamentally address these issues. We will share Microsoft Research's experience in developing Deep-Structured Semantic Models (DSSM) and their successful applications to web search, ads selection, machine translation, and entity search.
Views: 1341 Microsoft Research
Text mining with correspondence analysis
 
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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: 3987 François Husson
Torsten Reimer on the value and benefits of text mining
 
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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: 316 InDigONetwork
INTRODUCTION TO DATA MINING IN HINDI
 
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find relevant notes at-https://viden.io/
Views: 96234 LearnEveryone
What are the benefits of Text and Data Mining? - Monica Ihli
 
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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: 142 OpenMinTeD
Text Analytics and Text Mining Explained by OdinText
 
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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: 24770 OdinText
Data Mining - Foundations of Learning to Rank: Needs & Challenges | Lectures On-Demand
 
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Ambuj Tewari - EECS at the University of Michigan The 4th University of Michigan Data Mining Workshop Sponsored by Computer Science and Engineering, Yahoo!, and Office of Research Cyberinfrastructure (ORCI) Faculty, staff, and graduate students working in the fields of data mining, broadly construed. This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
Views: 2996 Michigan Engineering
Semantic Web Mining
 
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Semantic Web Mining by Dr. S Yasodha
Views: 345 Krish eClasses
Quantitative Text Analysis for Social Scientists  A talk by Nicole Rae Baerg
 
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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: 276 SAGE
Data Mining Research Topics | Data Mining Research Project Topics
 
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Contact Best Matlab Simulations Projects http://matlabsimulations.com/
Barbara Plank | Keynote - Natural Language Processing: Challenges and Next Frontiers
 
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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: 2200 PyData
An Introduction to Temporal Databases
 
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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: 4116 Postgres Conference
Introduction to Data Mining: Types of Sampling
 
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In part four of data preprocessing, we discuss the different types of sampling such as random sampling, stratified sampling, sampling without and with replacement. And go into the issues of sample size. -- At Data Science Dojo, we're extremely passionate about data science. Our in-person data science training has been attended by more than 3200+ employees from over 600 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: http://bit.ly/2mKLNu1 See what our past attendees are saying here: http://bit.ly/2ozVo4j -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... -- Vimeo: https://vimeo.com/datasciencedojo
Views: 3632 Data Science Dojo
Instant Visualization in Every Step of Analysis - O'Reilly Webcast
 
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A webcast led by Karen Hsu of Datameer. Surveys reveal that concerns about data quality can create barriers for companies deploying Analytics and BI initiatives. How can you readily identify and correct data quality issues at every step of your big data analysis to ensure accurate insights into customer behavior? In this webcast, we'll discuss how IT and business users can leverage self-service visualizations to quickly spot and correct data anomalies throughout the analytic process. You will learn how to: - Continuously visualize a profile of your data to identify inconsistencies, incompleteness and duplicates in your data - Visualize machine learning and data mining, including clustering, decision tree analysis, column correlations and recommendations - Create self-service visualizations for business and IT users About Karen Hsu: Karen is Senior Director, Product Marketing at Datameer. With over 15 years of experience in enterprise software, Karen Hsu has co-authored 4 patents and worked in a variety of engineering, marketing and sales roles. Most recently she came from Informatica where she worked with the start-ups Informatica purchased to bring big data, data quality, master data management, B2B and data security solutions to market. Karen has a Bachelors of Science degree in Management Science and Engineering from Stanford University. @Karenhsumar About host Ben Lorica: Ben Lorica is the Chief Data Scientist at O'Reilly Media, Inc. He has applied Business Intelligence, Data Mining, Machine Learning and Statistical Analysis in a variety of settings including Direct Marketing, Consumer and Market Research, Targeted Advertising, Text Mining, and Financial Engineering. His background includes stints with an investment management company, internet startups, and financial services. Don't miss an upload! Subscribe! http://goo.gl/szEauh Stay Connected to O'Reilly Media by Email - http://goo.gl/YZSWbO Follow O'Reilly Media: http://plus.google.com/+oreillymedia https://www.facebook.com/OReilly https://twitter.com/OReillyMedia
Views: 869 O'Reilly
Multilingual Text Mining: Lost in Translation, Found in Native Language Mining - Rohini Srihari
 
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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.
PHD RESEARCH TOPIC IN WEB MINING
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topic-dependable-secure-computing/
Views: 515 PHD Projects
Mining Aircraft Data for Aircraft Safety (Nikunj Oza)
 
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DataEDGE Conference 2017 — UC Berkeley School of Information http://dataedge.ischool.berkeley.edu/2017/ In this talk, I will give an overview of our efforts to mine flight operations and trajectory data to look for previously-unknown safety issues and precursors to known safety issues. I will describe some of our results, the nature of our algorithms, and plans for expanding the scope of our work. . . . . . . . . . . . . . . . . . . Nikunj Oza Leader, Data Sciences Group NASA Ames Research Center Nikunj Oza is the leader of the Data Sciences Group at NASA Ames Research Center. He also leads a NASA project team, which applies data mining to aviation safety and operations problems. Dr. Oza's 50+ research papers represent his research interests, which include data mining, machine learning, anomaly detection, and their applications to Aeronautics and Earth Science. He received the Arch T. Colwell Award for co-authoring one of the five most innovative technical papers selected from 3300+ SAE technical papers in 2005. His data mining team received the 2010 NASA Aeronautics Research Mission Directorate Associate Administrator¹s Award for best technology achievements by a team. He is an Associate Editor for the peer-reviewed journal Information Fusion (Elsevier) and has served as organizer, senior program committee member, and program committee member of several data mining and machine learning conferences. He received his B.S. in Mathematics with Computer Science from MIT in 1994, and M.S. (in 1998) and Ph.D. (in 2001) in Computer Science from the University of California at Berkeley.
Tentative steps towards mining PhD theses
 
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Sara Gould, Development Manager, British Library Sara will talk about the Library’s recent participation in a national project to mine chemical compounds from the pages of PhD theses, describe some of the challenges in accessing theses for Text and Data Mining, and invite participants to ‘have a go’ at mining theses for new research purposes.
Text and Data Mining in History
 
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Some quick remarks on text and data mining in history for my introduction to digital history course.
Views: 181 Igorcats
Stream Data Mining: A Big Data Perspective
 
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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: 782 KDD2016 video
Preparing for the Era of Big Data
 
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Chair: Kenneth Couch, University of Connecticut "Are We Ready for the Era of Big Data? Issues and Challenges in Big Data Management in the Public Sector," presented by Minyoung Ku, State University of New York, Albany "'Big Data' Need Bigger Theory and Methods," presented by Trevor Self, International Monetary Fund (IMF) "Effective Teaching of Research and Analysis for Public Policy and Management: Big Data, Diversity in Quantitative Backgrounds and Active Learning," presented by Dahlia Remler, Baruch College, City University of New York "Innovative Methods for Text Mining and Document Classification: Big Data Analysis for Science Policy," presented by Evgeny Klochikhin, American Institutes for Research
Views: 175 APPAM Online
Prof. Stefan Rüger - Visual mining: interpreting image data
 
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“From Big Data to Smart Knowledge – Text and Data Mining in Science and Economy”, Conference in Cologne February 23 to 24 2015 www.textminingconference.de
International Journal of Data Mining & Knowledge Management Process
 
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International Journal of Data Mining & Knowledge Management Process (IJDKP) ISSN : 2230 - 9608 [Online] ; 2231 - 007X [Print] http://airccse.org/journal/ijdkp/ijdkp.html Call for papers :- Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the Journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Topics of interest include, but are not limited to, the following: Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining, Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks, Educational data mining. Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing, OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources. Paper Submission Authors are invited to submit papers for this journal through E-mail: [email protected] or [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit : http://airccse.org/journal/ijdkp/ijdkp.html
Views: 140 aircc journal
Wiley's Duncan Campbell introduces the text and data mining session at #alpsp15
 
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Over the past few years, discussions of text and data mining (TDM) in the scientific publishing community have focused on copyright and licensing issues as reflected in government policy and on use-cases from the biomedical and pharmaceutical sectors. This session took a broader view of TDM in order to demonstrate the great potential text mining and analysis techniques have for developing new insights and knowledge across a whole range of scholarly disciplines.
Views: 115 ALPSP
Using Unstructured Text Data to Quantify Customer Perception
 
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With the exponential growth of social media and new touchpoints, customers are interacting with brands and organizations at a much faster pace, generating volumes of unstructured data in the form of customer reviews, feedback, preferences, trends, etc. Other metadata such as demographic data, transaction data or point of sale data, when combined with unstructured data can help organizations better understand consumer behavior and market forces, at a much more granular and deeper level. This enables brands to make effective business decisions for profitable growth. In this webinar, attendees learned how unstructured data analytics adds value, reduces time and costs in the identification of key business problems, and helps organizations track the health of their brand image by monitoring emerging trends and identifying aspects of the business responsible for a particular customer sentiment. The webinar also covered and introduced our Unstructured Text Analytics Platform ("UTAP") which allows the automation of classification of unstructured text data to categories, enabling organizations to track customer categories/issues over a stipulated period of time, with faster and more efficient analysis of unstructured text data.
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