Handbook of Statistical Analysis and Data Mining Applications Book

Handbook of Statistical Analysis and Data Mining Applications


  • Author : Robert Nisbet
  • Publisher : Elsevier
  • Release Date : 2017-11-09
  • Genre: Mathematics
  • Pages : 822
  • ISBN 10 : 9780124166455
  • Total Read : 73
  • File Size : 14,6 Mb

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Handbook of Statistical Analysis and Data Mining Applications Summary:

Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. Includes input by practitioners for practitioners Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models Contains practical advice from successful real-world implementations Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications

Handbook of Statistical Analysis and Data Mining Applications Book

Handbook of Statistical Analysis and Data Mining Applications


  • Author : Robert Nisbet
  • Publisher : Academic Press
  • Release Date : 2009
  • Genre: Mathematics
  • Pages : 824
  • ISBN 10 : 0123747651
  • Total Read : 76
  • File Size : 5,6 Mb

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Handbook of Statistical Analysis and Data Mining Applications Summary:

This comprehensive professional reference for scientists, engineers, and researchers brings together in a single resource all the information a beginner will need to rapidly learn how to conduct data mining and the statistical analysis required to interpret the data once mined. A glossary of data mining terms provided in the appendix.

Outlines and Highlights for Handbook of Statistical Analysis and Data Mining Applications by Robert Nisbet Book

Outlines and Highlights for Handbook of Statistical Analysis and Data Mining Applications by Robert Nisbet


  • Author : Cram101 Textbook Reviews
  • Publisher : Academic Internet Pub Incorporated
  • Release Date : 2010-12-01
  • Genre: Education
  • Pages : 98
  • ISBN 10 : 1617442992
  • Total Read : 85
  • File Size : 7,5 Mb

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Outlines and Highlights for Handbook of Statistical Analysis and Data Mining Applications by Robert Nisbet Summary:

Never HIGHLIGHT a Book Again! Virtually all of the testable terms, concepts, persons, places, and events from the textbook are included. Cram101 Just the FACTS101 studyguides give all of the outlines, highlights, notes, and quizzes for your textbook with optional online comprehensive practice tests. Only Cram101 is Textbook Specific. Accompanys: 9780123747655 .

Practical Text Mining and Statistical Analysis for Non structured Text Data Applications Book

Practical Text Mining and Statistical Analysis for Non structured Text Data Applications


  • Author : Gary Miner
  • Publisher : Academic Press
  • Release Date : 2012-01-11
  • Genre: Mathematics
  • Pages : 1096
  • ISBN 10 : 9780123869791
  • Total Read : 88
  • File Size : 10,6 Mb

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Practical Text Mining and Statistical Analysis for Non structured Text Data Applications Summary:

The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and so on. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text that it contains diminishes, the value of text mining for information retrieval and search will increase dramatically. This comprehensive professional reference brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. The Handbook of Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications presents a comprehensive how- to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities. -Extensive case studies, most in a tutorial format, allow the reader to 'click through' the example using a software program, thus learning to conduct text mining analyses in the most rapid manner of learning possible -Numerous examples, tutorials, power points and datasets available via companion website on Elsevierdirect.com -Glossary of text mining terms provided in the appendix

The Handbook of Data Mining Book

The Handbook of Data Mining


  • Author : Nong Ye
  • Publisher : CRC Press
  • Release Date : 2003-04-01
  • Genre: Computers
  • Pages : 720
  • ISBN 10 : 9781410607515
  • Total Read : 61
  • File Size : 11,8 Mb

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The Handbook of Data Mining Summary:

Created with the input of a distinguished International Board of the foremost authorities in data mining from academia and industry, The Handbook of Data Mining presents comprehensive coverage of data mining concepts and techniques. Algorithms, methodologies, management issues, and tools are all illustrated through engaging examples and real-world

Practical Text Mining and Statistical Analysis for Non structured Text Data Applications Book

Practical Text Mining and Statistical Analysis for Non structured Text Data Applications


  • Author : Gary Miner
  • Publisher : Academic Press
  • Release Date : 2012-01-25
  • Genre: Mathematics
  • Pages : 1000
  • ISBN 10 : 9780123870117
  • Total Read : 61
  • File Size : 7,5 Mb

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Practical Text Mining and Statistical Analysis for Non structured Text Data Applications Summary:

Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. Winner of a 2012 PROSE Award in Computing and Information Sciences from the Association of American Publishers, this book presents a comprehensive how-to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities. The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and so on. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text that it contains diminishes, the value of text mining for information retrieval and search will increase dramatically. Extensive case studies, most in a tutorial format, allow the reader to 'click through' the example using a software program, thus learning to conduct text mining analyses in the most rapid manner of learning possible Numerous examples, tutorials, power points and datasets available via companion website on Elsevierdirect.com Glossary of text mining terms provided in the appendix

Advances in Data Mining  Applications and Theoretical Aspects Book

Advances in Data Mining Applications and Theoretical Aspects


  • Author : Petra Perner
  • Publisher : Springer
  • Release Date : 2012-07-09
  • Genre: Computers
  • Pages : 289
  • ISBN 10 : 9783642314889
  • Total Read : 86
  • File Size : 15,5 Mb

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Advances in Data Mining Applications and Theoretical Aspects Summary:

This book constitutes the refereed proceedings of the 12th Industrial Conference on Data Mining, ICDM 2012, held in Berlin, Germany in July 2012. The 22 revised full papers presented were carefully reviewed and selected from 97 submissions. The papers are organized in topical sections on data mining in medicine and biology; data mining for energy industry; data mining in traffic and logistic; data mining in telecommunication; data mining in engineering; theory in data mining; theory in data mining: clustering; theory in data mining: association rule mining and decision rule mining.

Ensemble Methods in Data Mining Book

Ensemble Methods in Data Mining


  • Author : Giovanni Seni
  • Publisher : Morgan & Claypool Publishers
  • Release Date : 2010
  • Genre: Computers
  • Pages : 108
  • ISBN 10 : 9781608452842
  • Total Read : 83
  • File Size : 15,9 Mb

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Ensemble Methods in Data Mining Summary:

"Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges -- from investment timing to drug discovery, and fraud detection to recommendation systems -- where predictive accuracy is more vital than model interpretability. Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly. After describing trees and their strengths and weaknesses, the authors provide an overview of regularization -- today understood to be a key reason for the superior performance of modern ensembling algorithms. The book continues with a clear description of two recent developments: Importance Sampling (IS) and Rule Ensembles (RE). IS reveals classic ensemble methods -- bagging, random forests, and boosting -- to be special cases of a single algorithm, thereby showing how to improve their accuracy and speed. REs are linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles, which is essential to applications such as credit scoring and fault diagnosis. Lastly, the authors explain the paradox of how ensembles achieve greater accuracy on new data despite their (apparently much greater) complexity."--Publisher's website.