Practical Data Mining

Practical Data Mining PDF Author: Jr., Monte F. Hancock
Publisher: CRC Press
ISBN: 1439868379
Category : Computers
Languages : en
Pages : 304

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Book Description
Used by corporations, industry, and government to inform and fuel everything from focused advertising to homeland security, data mining can be a very useful tool across a wide range of applications. Unfortunately, most books on the subject are designed for the computer scientist and statistical illuminati and leave the reader largely adrift in tech

Practical Data Mining

Practical Data Mining PDF Author: Jr., Monte F. Hancock
Publisher: CRC Press
ISBN: 1439868379
Category : Computers
Languages : en
Pages : 304

Get Book

Book Description
Used by corporations, industry, and government to inform and fuel everything from focused advertising to homeland security, data mining can be a very useful tool across a wide range of applications. Unfortunately, most books on the subject are designed for the computer scientist and statistical illuminati and leave the reader largely adrift in tech

Practical Applications of Data Mining

Practical Applications of Data Mining PDF Author: Sang Suh
Publisher: Jones & Bartlett Publishers
ISBN: 0763785873
Category : Computers
Languages : en
Pages : 436

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Book Description
Introduction to data mining -- Association rules -- Classification learning -- Statistics for data mining -- Rough sets and bayes theories -- Neural networks -- Clustering -- Fuzzy information retrieval.

Data Mining

Data Mining PDF Author: Ian H. Witten
Publisher: Elsevier
ISBN: 0080890369
Category : Computers
Languages : en
Pages : 665

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Book Description
Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Introduction to Data Mining and its Applications

Introduction to Data Mining and its Applications PDF Author: S. Sumathi
Publisher: Springer
ISBN: 3540343512
Category : Computers
Languages : en
Pages : 828

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Book Description
This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in database systems, and presents a broad, yet in-depth overview of the field of data mining. Data mining is a multidisciplinary field, drawing work from areas including database technology, artificial intelligence, machine learning, neural networks, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing and data visualization.

Data Mining for Business Applications

Data Mining for Business Applications PDF Author: Carlos A. Mota Soares
Publisher: IOS Press
ISBN: 1607506327
Category : Computers
Languages : en
Pages : 196

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Book Description
Data mining is already incorporated into the business processes in sectors such as health, retail, automotive, finance, telecom and insurance as well as in government. This book contains extended versions of a selection of papers presented at a series of workshops held between 2005 and 2008 on the subject of data mining for business applications.

Applications of Data Mining in E-business and Finance

Applications of Data Mining in E-business and Finance PDF Author: Carlos A. Mota Soares
Publisher: IOS Press
ISBN: 1586038907
Category : Business & Economics
Languages : en
Pages : 156

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Book Description
Contains extended versions of a selection of papers presented at the workshop Data mining for business, held in 2007 together with the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Nanjing China--Preface.

Handbook of Statistical Analysis and Data Mining Applications

Handbook of Statistical Analysis and Data Mining Applications PDF Author: Robert Nisbet
Publisher: Elsevier
ISBN: 0124166458
Category : Mathematics
Languages : en
Pages : 822

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Book Description
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

Scientific Data Mining

Scientific Data Mining PDF Author: Chandrika Kamath
Publisher: SIAM
ISBN: 0898717698
Category : Mathematics
Languages : en
Pages : 295

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Book Description
Chandrika Kamath describes how techniques from the multi-disciplinary field of data mining can be used to address the modern problem of data overload in science and engineering domains. Starting with a survey of analysis problems in different applications, it identifies the common themes across these domains.

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

Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications PDF Author: Gary Miner
Publisher: Academic Press
ISBN: 012386979X
Category : Computers
Languages : en
Pages : 1096

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Book Description
"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"--

A Practical Guide to Data Mining for Business and Industry

A Practical Guide to Data Mining for Business and Industry PDF Author: Andrea Ahlemeyer-Stubbe
Publisher: John Wiley & Sons
ISBN: 1118763378
Category : Mathematics
Languages : en
Pages : 328

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Book Description
Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest.