Home
Search results “Conference papers on data mining”
2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP)
 
07:54:10
2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), August 21-25, 2018, Lviv, Ukraine. Opening, Conference Hall, «Pivdennyi-EXPO».
Views: 577 Dsmp Conference
Data Mining Projects 2016-2017 | ieee data mining papers 2016
 
00:55
ieee data mining papers 2016 for ME,M.Tech.,M.Phil., Ph.D., B.E, B.Tech., MCA A Novel Recommendation Model Regularized with User Trust and Item Ratings Automatically Mining Facets for Queries from Their Search Results Booster in High Dimensional Data Classification Building an intrusion detection system using a filter-based feature selection algorithm Connecting Social Media to E-Commerce: Cold-Start Product Recommendation Using Microblogging Information Cross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings Crowdsourcing for Top-K Query Processing over Uncertain Data Cyberbullying Detection based on Semantic-Enhanced Marginalized Denoising Auto-Encoder Domain-Sensitive Recommendation with User-Item Subgroup Analysis Efficient Algorithms for Mining Top-K High Utility Itemsets Efficient Cache-Supported Path Planning on Roads Mining User-Aware Rare Sequential Topic Patterns in Document Streams Nearest Keyword Set Search in Multi-Dimensional Datasets Rating Prediction based on Social Sentiment from Textual Reviews Location Aware Keyword Query Suggestion Based on Document Proximity Using Hashtag Graph-based Topic Model to Connect Semantically-related Words without Co-occurrence in Microblogs Quantifying Political Leaning from Tweets, Retweets, and Retweeters Relevance Feedback Algorithms Inspired By Quantum Detection Sentiment Embeddings with Applications to Sentiment Analysis Top-Down XML Keyword Query Processing TopicSketch: Real-time Bursty Topic Detection from Twitter Top-k Dominating Queries on Incomplete Data Understanding Short Texts through Semantic Enrichment and Hashing To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: www.jpinfotech.org, Blog: www.jpinfotech.blogspot.com
Views: 2449 JPINFOTECH PROJECTS
2018 IEEE Transaction Paper on Data Mining: Easy Paper to implement set 2
 
04:32
Paper 21: Weak Classifier for Density Estimation in Eye Localization and Tracking. Paper 22: Segmentation- and Annotation-Free License Plate Recognition With Deep Localization and Failure Identification. Paper 23: Features Classification Forest: A Novel Development that is Adaptable to Robust Blind Watermarking Techniques. Paper 24: Single Image Super-Resolution via Adaptive Transform-Based Nonlocal Self-Similarity Modeling and Learning-Based Gradient Regularization. Paper 25: Steganography with Multiple JPEG Images of the Same Scene. Paper 26: Affine Non-local Means Image Denoising. Paper 27: Higher Order Dynamic Conditional Random Fields Ensemble for Crop Type Classification in Radar Images. Paper 28: Single Image Rain Streak Decomposition Using Layer Priors. Paper 29: Fractional Krawtchouk transform with an application to image watermarking. Paper 30: Hierarchical Guidance Filtering-Based Ensemble Classification for Hyperspectral Images. Paper 31: A Hierarchical Approach for Rain or Snow Removing in A Single Color Image. Paper 32: Contrast Enhancement Based on Intrinsic Image Decomposition.
Views: 131 Ashwini Cly
2018 IEEE International Conference on Data Stream Mining & Processing
 
05:25
2018 IEEE International Conference on Data Stream Mining & Processing, August 21-25, 2018, Lviv
Views: 240 Dsmp Conference
SookYoung Son  | South Korea | Big Data Analysis and Data Mining  2015 | Conference Series LLC
 
14:23
2nd International Conference on Big Data Analysis and Data Mining November 30-December 01, 2015 San Antonio, USA Scientific Talk On: A case study on the application of process mining techniques in offshore plant construction process analysis Click here for Abstract and Biography: http://datamining.conferenceseries.com/speaker/2015/sookyoung-son-hyundai-heavy-industries-south-korea Conferenceseries LLC : http://www.conferenceseries.com Omics International : http://www.omicsonline.org/
ieee explore paper free : Data Mining with Big Data
 
01:48
we open up opportunities to find the papers you want. papers which may be we get are from the IEEE, ACM, ACME, ISI, KNOVEL, Proquest, and others. http://ieeexploredownload.blogspot.com/ its free. put your mail and link paper on coment below
An Internal Intrusion Detection and Protection System by Using Data Mining and Forensic Techniques
 
10:22
Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 813 Clickmyproject
2018 IEEE Transaction Papers:Datamining top 7 easy understanding and implementable papers
 
03:30
These papers easy to understand and implement. Paper 1: Mining Competitors from Large Unstructured Datasets. Paper 2: Hierarchy-Cutting Model based Association Semantic for Analyzing Domain Topic on the Web. paper 3: Efficient Keyword-Aware Representative Travel Route Recommendation. Paper 4: Learning from Cross-Domain Media Streams for Event-of-Interest Discovery. Paper 5: Mining Fashion Outfit Composition Using An End-to-End Deep Learning Approach on Set Data. Paper 6: Building and Querying an Enterprise Knowledge Graph. Paper 7: Image Re-ranking based on Topic Diversity.
Views: 305 Ashwini Cly
[ACSIC Speaker Series #5] Writing Research Papers for Premier Forums in Knowledge and Data Engine...
 
01:37:17
Time: Jan. 22nd, 10:00--11:30am, EST Title:  Writing Research Papers for Premier Forums in Knowledge and Data Engineering Presenter: Xindong Wu is a Professor of Computer Science at the University of Vermont (USA), and a Fellow of the IEEE and the AAAS. He holds a PhD in Artificial Intelligence from the University of Edinburgh, Britain. His research interests include data mining, Big Data analytics, knowledge engineering, and Web systems. He has published over 370 refereed papers in these areas in various journals and conferences, including IEEE TPAMI, TKDE, ACM TOIS, KAIS, DMKD, IJCAI, AAAI, ICML, KDD, ICDM, and WWW, as well as 40 books and conference proceedings. He is Steering Committee Chair of the IEEE International Conference on Data Mining (ICDM), Editor-in-Chief of Knowledge and Information Systems (KAIS, by Springer), and Editor-in-Chief of the Springer Book Series on Advanced Information and Knowledge Processing (AI&KP). He was the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (TKDE, by the IEEE Computer Society) between January 1, 2005 and December 31, 2008. He has served as Program Committee Chair/Co-Chair for ICDM '03 (the 2003 IEEE International Conference on Data Mining), KDD-07 (the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), CIKM 2010 (the 19th ACM Conference on Information and Knowledge Management), and ASONAM 2014 (the 2014 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining). Professor Wu is the 2004 ACM SIGKDD Service Award winner and the 2006 IEEE ICDM Outstanding Service Award winner. He received the 2012 IEEE Computer Society Technical Achievement Award "for pioneering contributions to data mining and applications", and the 2014 IEEE ICDM 10-Year Highest-Impact Paper Award.
Views: 1640 Acsic People
Java in production for Data Mining Research projects (JavaDayKiev'15)
 
51:01
Alexey Zinoviev presented this paper on the JavaDayKiev'15 conference Slides: http://www.slideshare.net/zaleslaw/javadaykiev15-java-in-production-for-data-mining-research-projects This paper covers next topics: Data Mining, Machine Learning, Hadoop, Spark, MLlib
Views: 318 Alexey Zinoviev
Data Mining Group Project Presentation
 
14:26
TEDx Style Presentation
Views: 92 Rapid Analytics
To improve Blood Donation Process using Data Mining Techniques | Final Year Projects 2016
 
07:56
Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 816 Clickmyproject
Data Mining Techniques for Detecting Behavioral Patterns of Gifted Students in Online Learning
 
04:49
The paper entitled "Data Mining Techniques for Detecting Behavioral Patterns of Gifted Students in Online Learning Environment (Case Study)" will be presented in the framework of the fourth edition of the international conference "The Future of Education" that will be held in Florence on 12 - 13 June 2014
Views: 231 Pixel Conferences
Final Year Projects 2015 |  Soil Classification Using  Data Mining
 
07:01
Including Packages ===================== * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 1340 Clickmyproject
Soil Classification Using  Data Mining  Techniques | Final Year Projects 2016
 
08:00
Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://myprojectbazaar.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
Views: 126 myproject bazaar
Java in production for Data Mining Research projects (JET'15, Minsk)
 
57:22
Alexey Zinoviev presented this paper on the JET conference Slides: http://www.slideshare.net/zaleslaw/javadaykiev15-java-in-production-for-data-mining-research-projects This paper covers next topics: Data Mining, Machine Learning, Hadoop, Spark, MLlib
Views: 168 Alexey Zinoviev
Diagnosis of Lung Cancer Prediction System Using Data Mining Classification Techniques
 
08:39
Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 6030 Clickmyproject
Data Analytics - Mining Streams - I
 
30:01
Subject:Computer Science Paper: Data analytics
Views: 103 Vidya-mitra
Second International Conference on Data Mining & Knowledge Management Process (DKMP 2014)
 
00:59
The Second International Conference on Data Mining & Knowledge Management Process (DKMP 2014) is jointly organized by AIRCC's Computer Science & Information Technology Community (CSITC) and Vel Tech Dr. RR & Dr. SR Technical University, Chennai, India. Second International Conference on Data Mining & Knowledge Management Process (DKMP 2014) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Data Mining and knowledge management process. The goal of this conference is to bring together researchers and practitioners from academia and industry to focus on understanding Modern data mining concepts and establishing new collaborations in these areas. http://airccj.org/2014/dkmp/index.html
Views: 149 IJNGN
Mining Aircraft Data for Aircraft Safety (Nikunj Oza)
 
33:38
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.
A survey Big Data social media using data mining techniques | | Final Year Projects 2016 - 2017
 
09:30
Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/clickmyproject Mail Us: [email protected]
Views: 137 Clickmyproject
2nd International Conference on Big Data Analysis and Data Mining
 
02:03
Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations.
Multimove - A Trajectory Data Mining Tool
 
02:17
2013 - Mining Representative Movement Patterns through Compression NhatHai Phan, Dino Ienco, Pascal Poncelet, and Maguelonne Teisseire. The 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013), Goal Coast, Australia, April 2013. (acceptance rate: 11.3%) 2012 - Mining Time Relaxed Gradual Moving Object Clusters NhatHai Phan, Dino Ienco, Pascal Poncelet, and Maguelonne Teisseire. In Proceedings of the 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS 2012), Redondo Beach, California, November 2012. [pdf] [demo] [code] (acceptance rate: 22%) 2012 - GeT_Move: An Efficient and Unifying Spatio-Temporal Pattern Mining Algorithm for Moving Objects NhatHai Phan, Pascal Poncelet, and Maguelonne Teisseire. In Proceedings of the 11th International Symposium on Intelligent Data Analysis (IDA 2012), Helsinki, Finland, October 2012. 2012 - Extracting Trajectories through an Efficient and Unifying Spatio-Temporal Pattern Mining System NhatHai Phan, Dino Ienco, Pascal Poncelet, and Maguelonne Teisseire. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2012), Demo Paper, Bristol, UK, September 2012.
Views: 499 nhathai phan
Dominik Slezak  |  Poland | Big Data Analysis and Data Mining  2015 | Conference Series LLC
 
01:16:08
2nd International Conference on Big Data Analysis and Data Mining November 30-December 01, 2015 San Antonio, USA Scientific Talk On: Knowledge Pit Platform for Modern Data Mining Competitions Click here for Abstract and Biography: http://datamining.conferenceseries.com/speaker/2015/dominik-slezak-university-of-warsaw-poland Conferenceseries LLC : http://www.conferenceseries.com Omics International : http://www.omicsonline.org/
International Journal of Data Mining & Knowledge Management Process (IJDKP)
 
00:36
International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] 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 workshop 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. 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 Mmodeling, 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] . Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.
Views: 172 ijdkp jou
ASEE MidAtlantic Conference Talk on Educational Data Mining
 
01:18
Washington D.C. ASEE Conference giving talk
A Systematic Review on Educational Data Mining
 
10:45
Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/clickmyproject Mail Us: [email protected]
Views: 33 Clickmyproject
Concept Drift Detector in Data Stream Mining
 
25:21
Jorge Casillas, Shuo Wang, Xin Yao, Concept Drift Detection in Histogram-Based Straightforward Data Stream Classification, 6th International Workshop on Data Science and Big Data Analytics, IEEE International Conference on Data Mining, November 17-20, 2018 - Singapore http://decsai.ugr.es/~casillas/downloads/papers/casillas-ci44-icdm18.pdf This presentation shows a novel algorithm to accurately detect changes in non-stationary data streams in a very efficiently way. If you want to know how the yacare caiman, the cheetah and the racer snake are related to this research, do not stop watching the video! More videos here: http://decsai.ugr.es/~casillas/videos.html
Views: 59 Jorge Casillas
Vista Analytics at the 2017 IEEE International Conference on Big Data
 
21:54
Yihua Shi Astle presents "Application of Dynamic Logistic Regression with Unscented Kalman Filter in Predictive Coding" which is a paper accepted by the conference co-authored with Vista co-founder Craig Freeman and Dr. Xuning Tang
Views: 155 Vista Analytics
AN INVESTIGATION OF STUDENTS BEHAVIOR IN DISCUSSION FORUMS USING EDUCATIONAL DATA MINING.
 
14:09
Presentation of published research at the Twenty-Eighth International Conference on Software Engineering and Knowledge Engineering (SEKE 2016).
Views: 157 Crystiano Jose
A Data Mining Framework for Valuing Large Portfolios of Variable Annuities
 
02:19
A promotional video for the paper "A Data Mining Framework for Valuing Large Portfolios of Variable Annuities" accepted by the 23rd SIGKDD conference.
Views: 22 Guojun Gan
A New Methodology for Mining Frequent Itemsets on Temporal Data
 
12:56
Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/clickmyproject Mail Us: [email protected]
Views: 55 Clickmyproject
KDD2016 paper 1044
 
02:33
Title: Crime Rate Inference with Big Data Authors: Hongjian Wang*, Penn State University Zhenhui L, Penn State University Daniel Kifer, Penn State University Corina Graif, Penn State University Abstract: Crime is one of the most important social problems in the country, affecting public safety, children development, and adult socioeconomic status. Understanding what factors cause higher crime is critical for policy makers in their efforts to reduce crime and increase citizens’ life quality. We tackle a fundamental problem in our paper: crime rate inference at the neighborhood level. Traditional approaches have used demographics and geographical influences to estimate crime rates in a region. With the fast development of positioning technology and prevalence of mobile devices, a large amount of modern urban data have been collected and such big data can provide new perspectives for understanding crime. In this paper, we used large-scale Point-Of-Interest data and taxi flow data in the city of Chicago, IL. We observed significantly improved performance in crime rate inference compared to using traditional features. Such an improvement is consistent over multiple years. We also showed that these new features are significant in the feature importance analysis. More on http://www.kdd.org/kdd2016/ KDD2016 Conference will be recorded and published on http://videolectures.net/
Views: 289 KDD2016 video
KDD2016 paper 575
 
03:39
Title: Dynamic and Robust Wildfire Risk Prediction System: An Unsupervised Approach Authors: Mahsa Salehi*, IBM Australia Laura Rusu, IBM Research Timothy Lynar, IBM Research Anna Phan, IBM Research Abstract: Ability to predict the risk of damaging weather events (e.g. wildfires) is crucial in helping emergency services in their decision-making processes, to mitigate and reduce the severity of such events. Today, wildfire rating systems have been in operation extensively in many countries around the world to estimate the danger of wildfires. In this paper we propose a data-driven approach to predict the wildfire risk. We show how we address the inherent challenges of such an approach that arise mainly due to the temporal dynamicity of weather data. Weather observations naturally change in time, with finer-scale variation (e.g. stationary day or stationary night) or large variations (non-stationary day or night), and this determines a temporal variation of the predicted fire danger. We show how our dynamic wildfire danger prediction model addresses the aforementioned challenges using context-based anomaly detection techniques and can be customized to different regions. We call our predictive model a Context-Based Fire Risk (CBFR) model. The advantage of our model is that it maintains multiple historical models for different temporal variations (e.g. day versus night), and use ensemble learning techniques to predict wildfire risk with high accuracy. In addition, it is completely unsupervised and does not rely on expert knowledge, which makes it flexible and easy to be applied in any region of interest. Our CBFR model is also scalable and can potentially be parallelized to speed up computation. Considering multiple wildfires (a.k.a. bushfires in Australia) locations in the Blue Mountains 2013 bushfires, Australia as a case study, we have compared the results of our system with the existing well-established Australian bushfire rating system. The experimental results show that our predictive model has a substantially higher accuracy in predicting the fire risk, which makes it an effective model to supplement the operational Australian bushfire rating system. More on http://www.kdd.org/kdd2016/ KDD2016 Conference will be recorded and published on http://videolectures.net/
Views: 4282 KDD2016 video
KDD2016 paper 813
 
02:24
Title: Lossless Separation of Web Pages into Layout Code and Data Authors: Adi Omari*, Technion Benny Kimelfeld, Technion Sharon Shoham, Academic College of Tel Aviv Yaffo Eran Yahav, Technion Abstract: A modern web page is often served by running layout code on data, producing an HTML document that enhances the data with front/back matters and layout/style operations. In this paper, we consider the opposite task: separating a given web page into a data component and a layout program. This separation has various important applications: page encoding may be significantly more compact (reducing web traffic), data representation is normalized across web designs (facilitating wrapping, retrieval and extraction), and repetitions are diminished (expediting site updates and redesign). We present a framework for defining the separation task, and devise an algorithm for synthesizing layout code from a web page while distilling its data in a lossless manner. The main idea is to synthesize layout code hierarchically for parts of the page, and use a combined program-data representation cost to decide whether to align intermediate programs. When intermediate programs are aligned, they are transformed into a single program, possibly with loops and conditionals. At the same time, differences between the aligned programs are captured by the data component such that executing the layout code on the data results in the original page. We have implemented our approach and conducted a thorough experimental study of its effectiveness. Our experiments show that our approach features state of the art (and higher) performance in both size compression and record extraction. More on http://www.kdd.org/kdd2016/ KDD2016 Conference will be recorded and published on http://videolectures.net/
Views: 879 KDD2016 video
Algorithmic Bias: From Discrimination Discovery to Fairness-Aware Data Mining (Part 1)
 
35:12
Authors: Carlos Castillo, EURECAT, Technology Centre of Catalonia Francesco Bonchi, ISI Foundation Abstract: Algorithms and decision making based on Big Data have become pervasive in all aspects of our daily lives lives (offline and online), as they have become essential tools in personal finance, health care, hiring, housing, education, and policies. It is therefore of societal and ethical importance to ask whether these algorithms can be discriminative on grounds such as gender, ethnicity, or health status. It turns out that the answer is positive: for instance, recent studies in the context of online advertising show that ads for high-income jobs are presented to men much more often than to women [Datta et al., 2015]; and ads for arrest records are significantly more likely to show up on searches for distinctively black names [Sweeney, 2013]. This algorithmic bias exists even when there is no discrimination intention in the developer of the algorithm. Sometimes it may be inherent to the data sources used (software making decisions based on data can reflect, or even amplify, the results of historical discrimination), but even when the sensitive attributes have been suppressed from the input, a well trained machine learning algorithm may still discriminate on the basis of such sensitive attributes because of correlations existing in the data. These considerations call for the development of data mining systems which are discrimination-conscious by-design. This is a novel and challenging research area for the data mining community. The aim of this tutorial is to survey algorithmic bias, presenting its most common variants, with an emphasis on the algorithmic techniques and key ideas developed to derive efficient solutions. The tutorial covers two main complementary approaches: algorithms for discrimination discovery and discrimination prevention by means of fairness-aware data mining. We conclude by summarizing promising paths for future research. More on http://www.kdd.org/kdd2016/ KDD2016 conference is published on http://videolectures.net/
Views: 1628 KDD2016 video
Feature Selection for reducing the dimensionality :Data mining
 
24:00
Paper ID 208 of ICSCC2017, NIT Kurukshetra Conference
Views: 45 Sai Prasad
A Systematic Review on Educational Data Mining | Final year Projects 2016 - 2017
 
19:41
Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
Views: 442 Clickmyproject
Using Data Mining in Forecasting Problems
 
41:53
In this presentation, Analytics 2012 keynote speaker, Tim Rey from Dow Chemical Company, shares methodologies for using data mining to get the most value out of time series data.
Views: 8835 SAS Software
KDD2016 paper 1027
 
02:51
Title: A Non-parametric Approach to Detect Epileptogenic Lesions using Restricted Boltzmann Machines Authors: Yijun Zhao*, Tufts University Bilal Ahmed, Tufts University Carla Brodley, Northeastern University Jennifer Dy, Northeastern University Abstract: Visual detection of lesional areas on a cortical surface is critical in rendering a successful surgical operation for Treatment Resistant Epilepsy (TRE) patients. Unfortunately, 45% of Focal Cortical Dysplasia (FCD, the most common kind of TRE) patients have no visual abnormalities in their brains. More on http://www.kdd.org/kdd2016/ KDD2016 Conference will be recorded and published on http://videolectures.net/
Views: 284 KDD2016 video
Feras Naser - IEEE Big Data 2018 -  A pathway to blockchain demonstrations
 
15:11
In this video, Feras will be showing his presentation on his review of some of the work that has been conducted in the area of blockchain and IoT. He will be doing this as part of his paper presentation in the IEEE Big Data conference, the paper that Feras has written is titled : REVIEW : THE POTENTIAL USE OF BLOCKCHAIN TECHNOLOGY IN RAILWAY APPLICATIONS AN INTRODUCTION OF A MOBILITY AND SPEECH RECOGNITION PROTOTYPE. In this video, Feras talks about two novel demonstrations, two speech recogntion data collection systems that can pave the way for further data integration and block chain implementation. Please do not hesitate to contact, if you have any question : [email protected] 00962791305808
Views: 23 Feras Shadid
A Review Paper on Dengue Disease Forcasting Using Data Mining Techniques
 
08:46
Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/clickmyproject Mail Us: [email protected]
Views: 28 Clickmyproject
KDD2016 paper 392
 
02:48
Title: Large-Scale Item Categorization in e-Commerce Using Multiple Recurrent Neural Networks Authors: Jung-Woo Ha*, NAVER LABS Hyuna Pyo, NAVER LABS Jeonghee Kim, NAVER LABS Abstract: Precise item categorization is a key issue in e-commerce domains. However, it still remains a challenging problem due to data size, category skewness, and noisy metadata. Here, we demonstrate a successful report on a deep learning-based item categorization method, i.e., deep categorization network (DeepCN), in an e-commerce website. DeepCN is an end-to-end model using multiple recurrent neural networks (RNNs) dedicated to metadata attributes for generating features from text metadata and fully connected layers for classifying item categories from the generated features. The categorization errors are propagated back through the fully connected layers to the RNNs for weight update in the learning process. This deep learning-based approach allows diverse attributes to be integrated into a common representation, thus overcoming sparsity and scalability problems. We evaluate DeepCN on large-scale real-world data including more than 94 million items with approximately 4,100 leaf categories from a Korean e-commerce website. Experiment results show our method improves the categorization accuracy compared to the model using single RNN as well as a standard classification model using unigram-based bag-of-words. Furthermore, we investigate how much the model parameters and the used attributes influence categorization performances. More on http://www.kdd.org/kdd2016/ KDD2016 Conference will be recorded and published on http://videolectures.net/
Views: 1550 KDD2016 video
KDD2016 paper 984
 
00:56
Title: The Limits of Popularity-Based Recommendations, and the Role of Social Ties Authors: Marco Bressan*, Sapienza University of Rome Stefano Leucci, Sapienza University of Rome Alessandro Panconesi, Sapienza University of Rome Prabhakar Raghavan, Google Erisa Terolli, Sapienza University of Rome Abstract: In this paper we introduce a mathematical model that captures some of the salient features of recommender systems that are based on popularity and that try to exploit social ties among the users. We show that, under very general conditions, the market always converges to a steady state, for which we are able to give an explicit form. Thanks to this we can tell rather precisely how much a market is altered by a recommendation system, and determine the power of users to influence others. Our theoretical results are complemented by experiments with real world social networks showing that social graphs prevent large market distortions in spite of the presence of highly influential users. More on http://www.kdd.org/kdd2016/ KDD2016 Conference will be recorded and published on http://videolectures.net/
Views: 3288 KDD2016 video
An Internal Intrusion Detection and Protection System - Data Mining and Forensic Techniques
 
09:55
Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 575 Clickmyproject

Ivadal 10 mg nebenwirkungen tamoxifen
Clarithromycin 250mg generic for biaxin
Atrovent udvs 2 ml to tsp
Blood pressure kit boots no7
3doodler mexico precio de cozaar