Results 231 to 240 of about 22,816,084 (277)

The WEKA data mining software: an update

SKDD, 2009
More than twelve years have elapsed since the first public release of WEKA. In that time, the software has been rewritten entirely from scratch, evolved substantially and now accompanies a text on data mining [35].
M. Hall, E. Frank, G. Holmes, Bernhard Pfahringer, P. Reutemann, I. Witten   +5 more
semanticscholar   +1 more source

microeco: An R package for data mining in microbial community ecology.

FEMS Microbiology Ecology, 2020
A large amount of sequencing data is produced in microbial community ecology studies using the high-throughput sequencing technique, especially amplicon-sequencing-based community data.
Chi Liu, Yaoming Cui, Xiangzhen Li, Minjie Yao   +3 more
semanticscholar   +1 more source

Data Mining

2016
Once three-dimensional reconstructions have been performed, most of the work required to access the information contained in the data set has to be done. Potential features of interest are numerous (eg, interfaces, particles, solid solution, clusters, and dislocations) and the relevance of the tool(s) to be used is tightly linked to the question to be ...
Lefebvre-Ulrikson, W., da Costa, Gérald, Rigutti, Lorenzo, Blum, Ivan   +3 more
openaire   +7 more sources

The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses

Current Protocols in Bioinformatics, 2016
GeneCards, the human gene compendium, enables researchers to effectively navigate and inter‐relate the wide universe of human genes, diseases, variants, proteins, cells, and biological pathways.
Gil Stelzer, Naomi Rosen, Inbar Plaschkes, Shahar Zimmerman, Michal Twik, Simon Fishilevich, Tsippi Iny Stein, R. Nudel, I. Lieder, Yaron Mazor, S. Kaplan, Dvir Dahary, D. Warshawsky, Yaron Guan-Golan, Asher Kohn, N. Rappaport, M. Safran, D. Lancet   +17 more
semanticscholar   +1 more source

From Data Mining to Knowledge Discovery in Databases

The AI Magazine, 1996
■ Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about?
U. Fayyad, G. Piatetsky-Shapiro, Padhraic Smyth   +2 more
semanticscholar   +1 more source

Educational data mining and learning analytics: An updated survey

WIREs Data Mining Knowl. Discov., 2020
This survey is an updated and improved version of the previous one published in 2013 in this journal with the title “data mining in education”. It reviews in a comprehensible and very general way how Educational Data Mining and Learning Analytics have ...
C. Romero, S. Ventura
semanticscholar   +1 more source

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

, 2004
In the words of the authors, the goal of this book was to “bring together many of the important new ideas in learning, and explain them in a statistical framework.” The authors have been quite successful in achieving this objective, and their work is a ...
D. Ruppert
semanticscholar   +1 more source

Data mining

Genetic Epidemiology, 2005
Group 14 used data-mining strategies to evaluate a number of issues, including appropriate diagnosis, haplotype estimation, genetic linkage and association studies, and type I error. Methods ranged from exploratory analyses, to machine learning strategies (neural networks, supervised learning, and tree-based methods), to false discovery rate control of
L Adrienne, Cupples, Julia, Bailey, Kevin C, Cartier, Catherine T, Falk, Kuang-Yu, Liu, Yuanqing, Ye, Robert, Yu, Heping, Zhang, Hongyu, Zhao   +8 more
openaire   +2 more sources

Fake News Detection on Social Media: A Data Mining Perspective

SKDD, 2017
Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media.
Kai Shu, A. Sliva, Suhang Wang, Jiliang Tang, Huan Liu   +4 more
semanticscholar   +1 more source

Data mining with big data

IEEE Transactions on Knowledge and Data Engineering, 2016
Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering ...
Xindong Wu, Xingquan Zhu, Gong-Qing Wu, Wei Ding   +3 more
semanticscholar   +1 more source