Commercial Data Mining Book

Commercial Data Mining


  • Author : David Nettleton
  • Publisher : Elsevier
  • Release Date : 2014-01-29
  • Genre: Computers
  • Pages : 304
  • ISBN 10 : 9780124166585

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Commercial Data Mining Excerpt :

Whether you are brand new to data mining or working on your tenth predictive analytics project, Commercial Data Mining will be there for you as an accessible reference outlining the entire process and related themes. In this book, you'll learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. Expert author David Nettleton guides you through the process from beginning to end and covers everything from business objectives to data sources, and selection to analysis and predictive modeling. Commercial Data Mining includes case studies and practical examples from Nettleton's more than 20 years of commercial experience. Real-world cases covering customer loyalty, cross-selling, and audience prediction in industries including insurance, banking, and media illustrate the concepts and techniques explained throughout the book. Illustrates cost-benefit evaluation of potential projects Includes vendor-agnostic advice on what to look for in off-the-shelf solutions as well as tips on building your own data mining tools Approachable reference can be read from cover to cover by readers of all experience levels Includes practical examples and case studies as well as actionable business insights from author's own experience

Commercial Data Mining Book

Commercial Data Mining


  • Author : David Nettleton
  • Publisher : Morgan Kaufmann
  • Release Date : 2014
  • Genre: Business & Economics
  • Pages : 288
  • ISBN 10 : 0124166024

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Commercial Data Mining Excerpt :

Whether you are brand new to data mining or working on your tenth predictive analytics project, Commercial Data Mining will be there for you as an accessible reference outlining the entire process and related themes. In this book, you'll learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. Expert author David Nettleton guides you through the process from beginning to end and covers everything from business objectives to data sources, and selection to analysis and predictive modeling. Commercial Data Mining includes case studies and practical examples from Nettleton's more than 20 years of commercial experience. Real-world cases covering customer loyalty, cross-selling, and audience prediction in industries including insurance, banking, and media illustrate the concepts and techniques explained throughout the book. Illustrates cost-benefit evaluation of potential projects Includes vendor-agnostic advice on what to look for in off-the-shelf solutions as well as tips on building your own data mining tools Approachable reference can be read from cover to cover by readers of all experience levels Includes practical examples and case studies as well as actionable business insights from author's own experience

Data Mining  Southeast Asia Edition Book
Score: 3.5
From 23 Ratings

Data Mining Southeast Asia Edition


  • Author : Jiawei Han
  • Publisher : Elsevier
  • Release Date : 2006-04-06
  • Genre: Computers
  • Pages : 800
  • ISBN 10 : 0080475582

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Data Mining Southeast Asia Edition Excerpt :

Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data— including stream data, sequence data, graph structured data, social network data, and multi-relational data. A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects Complete classroom support for instructors at www.mkp.com/datamining2e companion site

Data Warehousing and Data Mining Techniques for Cyber Security Book

Data Warehousing and Data Mining Techniques for Cyber Security


  • Author : Anoop Singhal
  • Publisher : Springer Science & Business Media
  • Release Date : 2007-04-06
  • Genre: Computers
  • Pages : 159
  • ISBN 10 : 9780387476537

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Data Warehousing and Data Mining Techniques for Cyber Security Excerpt :

The application of data warehousing and data mining techniques to computer security is an important emerging area, as information processing and internet accessibility costs decline and more and more organizations become vulnerable to cyber attacks. These security breaches include attacks on single computers, computer networks, wireless networks, databases, or authentication compromises. This book describes data warehousing and data mining techniques that can be used to detect attacks. It is designed to be a useful handbook for practitioners and researchers in industry, and is also suitable as a text for advanced-level students in computer science.

Introduction to Data Mining and Its Applications Book
Score: 3
From 2 Ratings

Introduction to Data Mining and Its Applications


  • Author : S. Sumathi
  • Publisher : Springer Science & Business Media
  • Release Date : 2006-09-26
  • Genre: Computers
  • Pages : 836
  • ISBN 10 : 9783540343509

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Introduction to Data Mining and Its Applications Excerpt :

This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in data base systems and new data base applications and is also designed to give 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, AI, machine learning, NN, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing and data visualization. This book is intended for a wide audience of readers who are not necessarily experts in data warehousing and data mining, but are interested in receiving a general introduction to these areas and their many practical applications. Since data mining technology has become a hot topic not only among academic students but also for decision makers, it provides valuable hidden business and scientific intelligence from a large amount of historical data. It is also written for technical managers and executives as well as for technologists interested in learning about data mining.

Data Mining and Statistics for Decision Making Book

Data Mining and Statistics for Decision Making


  • Author : Stéphane Tufféry
  • Publisher : John Wiley & Sons
  • Release Date : 2011-03-23
  • Genre: Computers
  • Pages : 716
  • ISBN 10 : 0470979283

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Data Mining and Statistics for Decision Making Excerpt :

Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives. This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, support vector machines, Vapnik theory, naive Bayesian classifier, ensemble learning and detection of association rules. They are discussed along with illustrative examples throughout the book to explain the theory of these methods, as well as their strengths and limitations. Key Features: Presents a comprehensive introduction to all techniques used in data mining and statistical learning, from classical to latest techniques. Starts from basic principles up to advanced concepts. Includes many step-by-step examples with the main software (R, SAS, IBM SPSS) as well as a thorough discussion and comparison of those software. Gives practical tips for data mining implementation to solve real world problems. Looks at a range of tools and applications, such as association rules, web mining and text mining, with a special focus on credit scoring. Supported by an accompanying website hosting datasets and user analysis. Statisticians and business intelligence analysts, students as well as computer science, biology, marketing and financial risk professionals in both commercial and government organizations across all business and industry sectors will benefit from this book.

Principles of Data Mining Book

Principles of Data Mining


  • Author : Max Bramer
  • Publisher : Springer
  • Release Date : 2016-11-09
  • Genre: Computers
  • Pages : 526
  • ISBN 10 : 9781447173076

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Principles of Data Mining Excerpt :

This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering. Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail. It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self study, this book aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. This expanded third edition includes detailed descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift.

Business Intelligence and Data Mining Book

Business Intelligence and Data Mining


  • Author : Anil Maheshwari
  • Publisher : Business Expert Press
  • Release Date : 2014-12-31
  • Genre: Business & Economics
  • Pages : 162
  • ISBN 10 : 9781631571213

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Business Intelligence and Data Mining Excerpt :

“This book is a splendid and valuable addition to this subject. The whole book is well written and I have no hesitation to recommend that this can be adapted as a textbook for graduate courses in Business Intelligence and Data Mining.” Dr. Edi Shivaji, Des Moines, Iowa “As a complete novice to this area just starting out on a MBA course I found the book incredibly useful and very easy to follow and understand. The concepts are clearly explained and make it an easy task to gain an understanding of the subject matter.” -- Mr. Craig Domoney, South Africa. Business Intelligence and Data Mining is a conversational and informative book in the exploding area of Business Analytics. Using this book, one can easily gain the intuition about the area, along with a solid toolset of major data mining techniques and platforms. This book can thus be gainfully used as a textbook for a college course. It is also short and accessible enough for a busy executive to become a quasi-expert in this area in a couple of hours. Every chapter begins with a case-let from the real world, and ends with a case study that runs across the chapters.

Data Mining Book

Data Mining


  • Author : John Wang
  • Publisher : IGI Global
  • Release Date : 2003-01-01
  • Genre: Computers
  • Pages : 484
  • ISBN 10 : 9781591400950

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Data Mining Excerpt :

Data Mining: Opportunities and Challenges presents an overview of the state of the art approaches in this new and multidisciplinary field of data mining. The primary objective of this book is to explore the myriad issues regarding data mining, specifically focusing on those areas that explore new methodologies or examine case studies. This book contains numerous chapters written by an international team of forty-four experts representing leading scientists and talented young scholars from seven different countries.

MASTERING DATA MINING  THE ART AND SCIENCE OF CUSTOMER RELATIONSHIP MANAGEMENT Book

MASTERING DATA MINING THE ART AND SCIENCE OF CUSTOMER RELATIONSHIP MANAGEMENT


  • Author : Michael J. A. Berry
  • Publisher : Unknown
  • Release Date : 2008-09-01
  • Genre: Uncategoriezed
  • Pages : 512
  • ISBN 10 : 8126518251

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MASTERING DATA MINING THE ART AND SCIENCE OF CUSTOMER RELATIONSHIP MANAGEMENT Excerpt :

Special Features: · Best-in-class data mining techniques for solving critical problems in all areas of business· Explains how to pick the right data mining techniques for specific problems· Shows how to perform analysis and evaluate results· Features real-world examples from across various industry sectors· Companion Web site with updates on data mining products and service providers About The Book: Companies have invested in building data warehouses to capture vast amounts of customer information. The payoff comes with mining or getting access to the data within this information gold mine to make better business decisions. Readers and reviewers loved Berry and Linoff's first book, Data Mining Techniques, because the authors so clearly illustrate practical techniques with real benefits for improved marketing and sales. Mastering Data Mining takes off from there-assuming readers know the basic techniques covered in the first book, the authors focus on how to best apply these techniques to real business cases. They start with simple applications and work up to the most powerful and sophisticated examples over the course of about 20 cases. (Ralph Kimball used this same approach in his highly successful Data Warehouse Toolkit). As with their first book, Mastering Data Mining is sufficiently technical for database analysts, but is accessible to technically savvy business and marketing managers. It should also appeal to a new breed of database marketing managers.

Data Mining  Concepts  Methodologies  Tools  and Applications Book

Data Mining Concepts Methodologies Tools and Applications


  • Author : Management Association, Information Resources
  • Publisher : IGI Global
  • Release Date : 2012-11-30
  • Genre: Computers
  • Pages : 2120
  • ISBN 10 : 9781466624566

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Data Mining Concepts Methodologies Tools and Applications Excerpt :

Data mining continues to be an emerging interdisciplinary field that offers the ability to extract information from an existing data set and translate that knowledge for end-users into an understandable way. Data Mining: Concepts, Methodologies, Tools, and Applications is a comprehensive collection of research on the latest advancements and developments of data mining and how it fits into the current technological world.

2013 6th International Conference on BioMedical Engineering and Informatics  BMEI 2013  Book

2013 6th International Conference on BioMedical Engineering and Informatics BMEI 2013


  • Author : Anonim
  • Publisher : DEStech Publications, Inc
  • Release Date : 2014-01-07
  • Genre: Medical
  • Pages : 1269
  • ISBN 10 : 9781605951638

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2013 6th International Conference on BioMedical Engineering and Informatics BMEI 2013 Excerpt :

SPBEI 2013 aims to be an excellent platform to facilitate international exchange of state-ofthe- art research and practice in image, video, and signal processing, biomedical engineering, informatics, and their cross-intersection to catalyze innovative research ideas and to dissimilate new scientific discoveries. The nature of the research demands collaboration in medicine, biology, physics, engineering, computer science, and statistics; and SPBEI attempts to expedite and strengthen the exploration and systemization of interdisciplinary knowledge. This year, the conference received a large number of submissions around the globe, and all papers have been rigorously reviewed by a large number of peer reviewers who have spent tremendous amount of time and effort on the evaluations, with each paper receiving three to six reviews. We would like to thank all those who submitted papers for considerations, and we extend our sincere gratitude to all those who devoted their time and effort professionally to ensuring the high standards of the technical program, including the authors, committee members, peer reviewers, and session chairs.

Data Mining Book

Data Mining


  • Author : Mehmed Kantardzic
  • Publisher : John Wiley & Sons
  • Release Date : 2019-10-23
  • Genre: Computers
  • Pages : 672
  • ISBN 10 : 9781119516071

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Data Mining Excerpt :

Presents the latest techniques for analyzing and extracting information from large amounts of data in high-dimensional data spaces The revised and updated third edition of Data Mining contains in one volume an introduction to a systematic approach to the analysis of large data sets that integrates results from disciplines such as statistics, artificial intelligence, data bases, pattern recognition, and computer visualization. Advances in deep learning technology have opened an entire new spectrum of applications. The author—a noted expert on the topic—explains the basic concepts, models, and methodologies that have been developed in recent years. This new edition introduces and expands on many topics, as well as providing revised sections on software tools and data mining applications. Additional changes include an updated list of references for further study, and an extended list of problems and questions that relate to each chapter.This third edition presents new and expanded information that: • Explores big data and cloud computing • Examines deep learning • Includes information on convolutional neural networks (CNN) • Offers reinforcement learning • Contains semi-supervised learning and S3VM • Reviews model evaluation for unbalanced data Written for graduate students in computer science, computer engineers, and computer information systems professionals, the updated third edition of Data Mining continues to provide an essential guide to the basic principles of the technology and the most recent developments in the field.

Big Data in Context Book

Big Data in Context


  • Author : Thomas Hoeren
  • Publisher : Springer
  • Release Date : 2017-10-17
  • Genre: Law
  • Pages : 120
  • ISBN 10 : 9783319624617

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Big Data in Context Excerpt :

This book is open access under a CC BY 4.0 license. This book sheds new light on a selection of big data scenarios from an interdisciplinary perspective. It features legal, sociological and economic approaches to fundamental big data topics such as privacy, data quality and the ECJ’s Safe Harbor decision on the one hand, and practical applications such as smart cars, wearables and web tracking on the other. Addressing the interests of researchers and practitioners alike, it provides a comprehensive overview of and introduction to the emerging challenges regarding big data.All contributions are based on papers submitted in connection with ABIDA (Assessing Big Data), an interdisciplinary research project exploring the societal aspects of big data and funded by the German Federal Ministry of Education and Research.This volume was produced as a part of the ABIDA project (Assessing Big Data, 01IS15016A-F). ABIDA is a four-year collaborative project funded by the Federal Ministry of Education and Research. However the views and opinions expressed in this book reflect only the authors’ point of view and not necessarily those of all members of the ABIDA project or the Federal Ministry of Education and Research.