The Model Organism Databases (MODs) are working with the InterMine group to enable faster comparative studies and develop tools that make analysis accessible to the wider scientific community.
even in the most wildly optimistic projections, data mining isn't tenable for uncovering future terrorist plots. We're not trading privacy for security; we're giving up privacy and getting no security in return.
Baker provides us with a fascinating guide to the world of "The Numerati" who use the data we produce every day (click web pages, flip channels, drive through automatic toll booths, shop with credit cards, and make cell phone calls) to profile us as workers, shoppers, patients, voters, potential terrorists, and lovers.
Online repository of large data sets for researchers in knowledge discovery and data mining. includes Discrete Sequence Data, Image Data, Multivariate Data, Relational Data, Spatio-Temporal Data, Text (corpora), Time Series, Web Data (web pages and log files).
The Software Environment for the Advancement of Scholarly Research (SEASR), funded by the Andrew W. Mellon Foundation, provides a research and development environment capable of powering leading-edge digital humanities initiatives.
the data here is useful for testing classification / clustering, and the accuracy of indexing techniques. However the datasets are too small to make claims about the efficiency of indexing.
The Digging into Data Challenge is an international grant competition sponsored by four leading research agencies, the Joint Information Systems Committee (JISC1) from the United Kingdom, the National Endowment for the Humanities (NEH2) from the United States, the National Science Foundation (NSF3) from the United States, and the Social Sciences and Humanities Research Council (SSHRC4) from Canada.
GroupLens is a research lab in the Department of Computer Science and Engineering at the University of Minnesota. datasets include MovieLens, Wikilens, Book-Crossing, Jester Joke, EachMovie.
This work is in the general area of sentiment analysis, opinion extraction or opinion mining, and feature-based opinion summarization from the user-generated content or user-generated media on the Web, e.g., reviews, forum and group discussions, and blogs. The area is also closely related to sentiment classification.
DataSift provides very granular and modular ‘sifting’ functions from a wide range of social and web input feeds, augmenting them with sentiment analysis, storage and analytics to offer an unrivalled service platform which leverages the cloud and scales infinitely. The world is moving to streams, and consumers will consume and curate their own news. DataSift follows this paradigm shift and seeks to become the platform of choice for stream curation, consumption, and ultimately monetization. The end visualizations are unlimited and bounded only by your imagination.
The Datawrangling blog was put on the back burner last May while I focused on my startup. Now that I have some bandwidth again, I am getting back to work on several pet projects (including the Amazon EC2 Cluster).
Eine Seite, die Informationen zum Thema "Information Retrieval" anhand des Buches "Information Retrieval - Suchmodelle und Data-Mining-Verfahren für Textsammlungen und das Web" von Reginald Ferber (dpunkt.verlag, März 2003) bereitstellt.
Twitter wird sein frisch eingekauftes Echtzeit-DV-System Storm als Open Source veröffentlichen. Damit wird die Technik für die Parallelisierung von Datenbankabfragen für alle verfügbar.
exchange ideas & share knowledge, Free on-demand video lectures from world's leading and prominent scientists, research institutions, EU research projects.
Screen scrapper and parser service. Cost: "In the future, non-commercial and small uses will remain free. Pricing structure for bigger applications and for commercial uses will be announced in the future."
AideRSS is an intelligent assistant that saves time and keeps you on top of the latest news. We research every story and filter out the noise, allowing you to focus on what matters most
a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code.
Tutorial Slides by Andrew Moore. The links point to a set of tutorials on many aspects of statistical data mining, including the foundations of probability, the foundations of statistical data analysis, and most of the classic machine learning and data mining algorithms.
"People who bookmark the same things that I bookmark are likely to have similar interests and are thus likely to continue bookmarking interesting things in the future. Since the information on who bookmarked what URL is public, the process of finding people with similar interests can be automated."
I. Yoo, P. Alafaireet, M. Marinov, K. Pena-Hernandez, R. Gopidi, J. Chang, und L. Hua. Journal of medical systems, 36 (4):
2431-48(August 2012)6940<m:linebreak></m:linebreak>JID: 7806056; 2011/02/07 received; 2011/04/07 accepted; 2011/05/03 aheadofprint; ppublish;<m:linebreak></m:linebreak>Anàlisi de dades; Data mining; Introductori.
M. Brookhart, R. Wyss, J. Layton, und T. Stürmer. Circulation. Cardiovascular quality and outcomes, 6 (5):
604-11(September 2013)Propensity score; Introductori; CV; SAS.
I. Lipkovich, A. Dmitrienko, und R. B. Statistics in medicine, 36 (1):
136-196(Januar 2017)Comparacions múltiples; Anàlisi de subgrups; RCT; Online; Parcial.
F. Lemmerich, M. Becker, P. Singer, D. Helic, A. Hotho, und M. Strohmaier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016, Seite 965--974. ACM, (2016)
N. Brown, A. Altadmri, S. Sentance, und M. Kölling. Proceedings of the 2018 ACM Conference on International Computing Education Research, Seite 196--204. New York, NY, USA, ACM, (2018)
G. Xue, H. Zeng, Z. Chen, W. Ma, H. Zhang, und C. Lu. SIGIR '03: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, Seite 56--63. New York, NY, USA, ACM, (2003)
J. Zhou, C. Ding, und D. Androutsos. CASCON '06: Proceedings of the 2006 conference of the Center for Advanced Studies on Collaborative research, New York, NY, USA, ACM, (2006)
Gabriel, J. Yu, H. Liu, und P. Yu. KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, Seite 300--309. New York, NY, USA, ACM, (2007)