Data Analytics Models and Algorithms for Intelligent Data Analysis /

This book is a comprehensive introduction to the methods and algorithms of modern data analytics. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. T...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Runkler, Thomas A. (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Wiesbaden : Springer Fachmedien Wiesbaden : Imprint: Springer Vieweg, 2016.
Έκδοση:2nd ed. 2016.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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003 DE-He213
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100 1 |a Runkler, Thomas A.  |e author. 
245 1 0 |a Data Analytics  |h [electronic resource] :  |b Models and Algorithms for Intelligent Data Analysis /  |c by Thomas A. Runkler. 
250 |a 2nd ed. 2016. 
264 1 |a Wiesbaden :  |b Springer Fachmedien Wiesbaden :  |b Imprint: Springer Vieweg,  |c 2016. 
300 |a XII, 150 p. 66 illus.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
505 0 |a Data Analytics -- Data and Relations -- Data Preprocessing -- Data Visualization -- Correlation -- Regression -- Forecasting -- Classification -- Clustering. 
520 |a This book is a comprehensive introduction to the methods and algorithms of modern data analytics. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. This book has been used for more than ten years in the Data Mining course at the Technical University of Munich. Much of the content is based on the results of industrial research and development projects at Siemens. Content • Data Analytics • Data and Relations • Data Preprocessing • Data Visualization • Correlation • Regression • Forecasting • Classification • Clustering Target Groups Students of computer science, mathematics and engineering Data analytics practitioners The Author Thomas A. Runkler is Principal Research Scientist at Siemens Corporate Technology and Professor for Computer Science at the Technical University of Munich. 
650 0 |a Computer science. 
650 0 |a Data structures (Computer science). 
650 0 |a Data mining. 
650 1 4 |a Computer Science. 
650 2 4 |a Data Mining and Knowledge Discovery. 
650 2 4 |a Data Structures. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783658140748 
856 4 0 |u http://dx.doi.org/10.1007/978-3-658-14075-5  |z Full Text via HEAL-Link 
912 |a ZDB-2-SCS 
950 |a Computer Science (Springer-11645)