Data Analytics : Models and Algorithms for Intelligent Data Analysis / by Thomas A Runkler
By: Runkler, Thomas A.
Contributor(s): SpringerLink (Online service)
Material type:
E-bookPublisher: Wiesbaden : Springer Fachmedien Wiesbaden Vieweg, 2016Edition: 2nd ed.Description: 1 recurso en línea (XII, 150 p.) : 66 ilustraciones.ISBN: 9783658140755.Subject: Data mining
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Madrid Digital Acceso Electrónico (UEM) | Ciencias e Ingeniería | QA76.9.D343 R865 2016 EB (Browse shelf(Opens below)) | .i11598748 | Acceso electrónico | eBOOK .i11598748 |
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| QA76.9.D343 M555 2018 EB Topic Detection and Classification in Social Networks The Twitter Case | QA76.9.D343 M866 2016 EB Conformance Checking and Diagnosis in Process Mining : Comparing Observed and Modeled Processes | QA76.9.D343 R434 2015 EB Recent Advances in Information and Communication Technology 2015 Proceedings of the 11th International Conference on Computing and Information Technology (IC2IT) | QA76.9.D343 R865 2016 EB Data Analytics : Models and Algorithms for Intelligent Data Analysis | QA76.9.D343 S656 2016 EB Link Prediction in Social Networks : Role of Power Law Distribution | QA76.9.D343 T736 2017 EB Transparent Data Mining for Big and Small Data | QA76.9.D343 V468 2016 EB Pattern Mining with Evolutionary Algorithms |
Data Analytics -- Data and Relations -- Data Preprocessing -- Data Visualization -- Correlation -- Regression -- Forecasting -- Classification -- Clustering.
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. 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. The text is designed for undergraduate and graduate courses on data analytics for engineering, computer science, and math students. It is also suitable for practitioners working on data analytics projects. This book has been used for more than ten years in numerous courses at the Technical University of Munich, Germany, in short courses at several other universities, and in tutorials at scientific conferences. Much of the content is based on the results of industrial research and development projects at Siemens
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