Data Preparation For Data Mining
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Data Preparation For Data Mining
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Author : Dorian Pyle
language : en
Publisher: Morgan Kaufmann
Release Date : 1999-03-22
Data Preparation For Data Mining written by Dorian Pyle and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-03-22 with Computers categories.
This book focuses on the importance of clean, well-structured data as the first step to successful data mining. It shows how data should be prepared prior to mining in order to maximize mining performance.
Data Preparation For Data Mining Using Sas
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Author : Mamdouh Refaat
language : en
Publisher:
Release Date : 2010
Data Preparation For Data Mining Using Sas written by Mamdouh Refaat and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
Are you a data mining analyst, who spends up to 80% of your time assuring data quality, then preparing that data for developing and deploying predictive models? And do you find lots of literature on data mining theory and concepts, but when it comes to practical advice on developing good mining views find little "how toň information? And are you, like most analysts, preparing the data in SAS? This book is intended to fill this gap as your source of practical recipes. It introduces a framework for the process of data preparation for data mining, and presents the detailed implementation of each step in SAS. In addition, business applications of data mining modeling require you to deal with a large number of variables, typically hundreds if not thousands. Therefore, the book devotes several chapters to the methods of data transformation and variable selection. A complete framework for the data preparation process, including implementation details for each step. The complete SAS implementation code, which is readily usable by professional analysts and data miners. A unique and comprehensive approach for the treatment of missing values, optimal binning, and cardinality reduction. Assumes minimal proficiency in SAS and includes a quick-start chapter on writing SAS macros.
Data Preparation For Data Mining Using Sas
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Author : Mamdouh Refaat
language : en
Publisher: Elsevier
Release Date : 2010-07-27
Data Preparation For Data Mining Using Sas written by Mamdouh Refaat and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-07-27 with Computers categories.
Are you a data mining analyst, who spends up to 80% of your time assuring data quality, then preparing that data for developing and deploying predictive models? And do you find lots of literature on data mining theory and concepts, but when it comes to practical advice on developing good mining views find little "how to information? And are you, like most analysts, preparing the data in SAS?This book is intended to fill this gap as your source of practical recipes. It introduces a framework for the process of data preparation for data mining, and presents the detailed implementation of each step in SAS. In addition, business applications of data mining modeling require you to deal with a large number of variables, typically hundreds if not thousands. Therefore, the book devotes several chapters to the methods of data transformation and variable selection. - A complete framework for the data preparation process, including implementation details for each step. - The complete SAS implementation code, which is readily usable by professional analysts and data miners. - A unique and comprehensive approach for the treatment of missing values, optimal binning, and cardinality reduction. - Assumes minimal proficiency in SAS and includes a quick-start chapter on writing SAS macros.
Intelligent Data Warehousing
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Author : Zhengxin Chen
language : en
Publisher: CRC Press
Release Date : 2001-12-13
Intelligent Data Warehousing written by Zhengxin Chen and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-12-13 with Computers categories.
Effective decision support systems (DSS) are quickly becoming key to businesses gaining a competitive advantage, and the effectiveness of these systems depends on the ability to construct, maintain, and extract information from data warehouses. While many still perceive data warehousing as a subdiscipline of management information systems (MIS), in
Data Preparation For Analytics Using Sas
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Author : Gerhard Svolba
language : en
Publisher: SAS Institute
Release Date : 2006-11-27
Data Preparation For Analytics Using Sas written by Gerhard Svolba and has been published by SAS Institute this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-11-27 with Computers categories.
Written for anyone involved in the data preparation process for analytics, Gerhard Svolba's Data Preparation for Analytics Using SAS offers practical advice in the form of SAS coding tips and tricks, and provides the reader with a conceptual background on data structures and considerations from a business point of view. The tasks addressed include viewing analytic data preparation in the context of its business environment, identifying the specifics of predictive modeling for data mart creation, understanding the concepts and considerations of data preparation for time series analysis, using various SAS procedures and SAS Enterprise Miner for scoring, creating meaningful derived variables for all data mart types, using powerful SAS macros to make changes among the various data mart structures, and more!
Handbook Of Statistical Analysis And Data Mining Applications
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Author : Robert Nisbet
language : en
Publisher: Academic Press
Release Date : 2009-05-14
Handbook Of Statistical Analysis And Data Mining Applications written by Robert Nisbet and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-05-14 with Mathematics categories.
The Handbook of Statistical Analysis and Data Mining Applications 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 one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book 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 industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions. - Written "By Practitioners for Practitioners" - Non-technical explanations build understanding without jargon and equations - Tutorials in numerous fields of study provide step-by-step instruction on how to use supplied tools to build models - Practical advice from successful real-world implementations - Includes extensive case studies, examples, MS PowerPoint slides and datasets - CD-DVD with valuable fully-working 90-day software included: "Complete Data Miner - QC-Miner - Text Miner" bound with book
Intelligent Soft Computation And Evolving Data Mining Integrating Advanced Technologies
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Author : Wang, Leon Shyue-Liang
language : en
Publisher: IGI Global
Release Date : 2010-03-31
Intelligent Soft Computation And Evolving Data Mining Integrating Advanced Technologies written by Wang, Leon Shyue-Liang and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-03-31 with Technology & Engineering categories.
"This book provides a reference to researchers, practitioners, and students in both soft computing and data mining communities for generating creative ideas of securing and managing data mining"--Provided by publisher.
Encyclopedia Of Data Warehousing And Mining Second Edition
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Author : Wang, John
language : en
Publisher: IGI Global
Release Date : 2008-08-31
Encyclopedia Of Data Warehousing And Mining Second Edition written by Wang, John and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-08-31 with Computers categories.
There are more than one billion documents on the Web, with the count continually rising at a pace of over one million new documents per day. As information increases, the motivation and interest in data warehousing and mining research and practice remains high in organizational interest. The Encyclopedia of Data Warehousing and Mining, Second Edition, offers thorough exposure to the issues of importance in the rapidly changing field of data warehousing and mining. This essential reference source informs decision makers, problem solvers, and data mining specialists in business, academia, government, and other settings with over 300 entries on theories, methodologies, functionalities, and applications.
Data Preprocessing In Data Mining
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Author : Salvador García
language : en
Publisher: Springer
Release Date : 2014-08-30
Data Preprocessing In Data Mining written by Salvador García and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-08-30 with Technology & Engineering categories.
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. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data. This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering.
Data Preparation For Social Network Mining And Analysis
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Author : Yazhe Wang
language : en
Publisher:
Release Date : 2014
Data Preparation For Social Network Mining And Analysis written by Yazhe Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Data mining categories.
"This dissertation studies the problem of preparing good-quality social network data for data analysis and mining. Modern online social networks such as Twitter, Facebook, and LinkedIn have rapidly grown in popularity. The consequent availability of a wealth of social network data provides an unprecedented opportunity for data analysis and mining researchers to determine useful and actionable information in a wide variety of fields such as social sciences, marketing, management, and security. However, raw social network data are vast, noisy, distributed, and sensitive in nature, which challenge data mining and analysis tasks in storage, efficiency, accuracy, etc. Many mining algorithms cannot operate or generate accurate results on the vast and messy data. Thus social network data preparation deserves special attention as it processes raw data and transforms them into usable forms for data mining and analysis tasks. Data preparation consists of four main steps, namely data collection, data cleaning, data reduction, and data conversion, each of which deals with different challenges of the raw data. In this dissertation, we consider three important problems related to the data collection and data conversion steps in social network data preparation..."--Author's abstract.