Data Preprocessing

Data Preprocessing in Data Mining  eBooks & eLearning

Posted by interes at Nov. 14, 2014
Data Preprocessing in Data Mining

Data Preprocessing in Data Mining (Intelligent Systems Reference Library) by Salvador García and Julián Luengo
English | 2014 | ISBN: 331910246X | 320 pages | PDF | 8 MB

Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process.
Evolutionary Computation in Data Mining (Studies in Fuzziness and Soft Computing)

Evolutionary Computation in Data Mining (Studies in Fuzziness and Soft Computing)

9783540223702 | (3540223703) | Springer | 2004 | 15 MB | RS | FF

R for Data Science Cookbook  eBooks & eLearning

Posted by AlenMiler at April 30, 2017
R for Data Science Cookbook

R for Data Science Cookbook by Yu-Wei, Chiu (David Chiu)
English | 29 July 2016 | ASIN: B01ET5I38M | 452 Pages | AZW3 | 15.86 MB

Data Science Using Oracle Data Miner and Oracle R Enterprise  eBooks & eLearning

Posted by naag at Jan. 11, 2017
Data Science Using Oracle Data Miner and Oracle R Enterprise

Data Science Using Oracle Data Miner and Oracle R Enterprise: Transform Your Business Systems into an Analytical Powerhouse by sibanjan Das
English | 8 Feb. 2017 | ISBN: 1484226135 | 289 Pages | Epub | 6.34 MB

Data Science Using Oracle Data Miner and Oracle R Enterprise  eBooks & eLearning

Posted by hill0 at Dec. 23, 2016
Data Science Using Oracle Data Miner and Oracle R Enterprise

Data Science Using Oracle Data Miner and Oracle R Enterprise: Transform Your Business Systems into an Analytical Powerhouse by sibanjan Das
English | 8 Feb. 2017 | ISBN: 1484226135 | 289 Pages | PDF | 12.99 MB

Automate the predictive analytics process using Oracle Data Miner and Oracle R Enterprise. This book talks about how both these technologies can provide a framework for in-database predictive analytics. You'll see a unified architecture and embedded workflow to automate various analytics steps such as data preprocessing, model creation, and storing final model output to tables.

Next-Generation Sequencing Data Analysis  eBooks & eLearning

Posted by roxul at April 6, 2016
Next-Generation Sequencing Data Analysis

Xinkun Wang, "Next-Generation Sequencing Data Analysis"
English | ISBN: 1482217880 | 2016 | 258 pages | PDF | 14 MB

Spatial Data Mining  eBooks & eLearning

Posted by Underaglassmoon at March 31, 2016
Spatial Data Mining

Spatial Data Mining: Theory and Application
Springer | Computer Science, Textbook | April 24, 2016 | ISBN-10: 3662485362 | 308 pages | pdf | 10.82 mb

Authors: Li, Deren, Wang, Shuliang, Li, Deyi
Presents up-to-date work on core theories and applications of spatial data mining, combining the principles of data mining and geospatial information science
Proposes data fields, cloud model, and mining views methods, and presents empirical applications in the context of GIS and remote sensing

Data Mining: A Knowledge Discovery Approach  eBooks & eLearning

Posted by step778 at Feb. 3, 2016
Data Mining: A Knowledge Discovery Approach

Krzysztof J. Cios, Witold Pedrycz, Roman W. Swiniarski, "Data Mining: A Knowledge Discovery Approach"
2007 | pages: 600 | ISBN: 0387333339 | PDF | 11,5 mb
Quantitative Medical Data Analysis Using Mathematical Tools and Statistical Techniques (Repost)

Don Hong, Yu Shyr, "Quantitative Medical Data Analysis Using Mathematical Tools and Statistical Techniques"
2007 | pages: 364 | ISBN: 9812704612 | PDF | 7,2 mb

Data Analytics: Models and Algorithms for Intelligent Data Analysis (repost)  eBooks & eLearning

Posted by libr at Nov. 2, 2014
Data Analytics: Models and Algorithms for Intelligent Data Analysis (repost)

Data Analytics: Models and Algorithms for Intelligent Data Analysis by Thomas A. Runkler
English | 2012 | ISBN-10: 3834825883 | PDF | 147 pages | 2,5 MB

This book is a comprehensive introduction to the methods and algorithms and approaches of modern data analytics. It covers data preprocessing, visualization, correlation, regression, forecasting, classification, and clustering.