Commercial Data Mining

Commercial Data Mining
Author: David Nettleton
Publsiher: Morgan Kaufmann
Total Pages: 288
Release: 2014
ISBN 10: 9780124166028
ISBN 13: 0124166024
Language: EN, FR, DE, ES & NL

Commercial Data Mining Book Review:

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

Commercial Data Mining
Author: David Nettleton
Publsiher: Elsevier
Total Pages: 304
Release: 2014-01-29
ISBN 10: 012416658X
ISBN 13: 9780124166585
Language: EN, FR, DE, ES & NL

Commercial Data Mining Book Review:

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

Principles of Data Mining

Principles of Data Mining
Author: Max Bramer
Publsiher: Springer Nature
Total Pages: 571
Release: 2020-05-20
ISBN 10: 1447174933
ISBN 13: 9781447174936
Language: EN, FR, DE, ES & NL

Principles of Data Mining Book Review:

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, it 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. Principles of Data Mining includes 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. The expanded fourth edition gives a detailed description of a feed-forward neural network with backpropagation and shows how it can be used for classification.

Data Mining Southeast Asia Edition

Data Mining  Southeast Asia Edition
Author: Jiawei Han,Jian Pei,Micheline Kamber
Publsiher: Elsevier
Total Pages: 800
Release: 2006-04-06
ISBN 10: 9780080475585
ISBN 13: 0080475582
Language: EN, FR, DE, ES & NL

Data Mining Southeast Asia Edition Book Review:

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

Data Warehousing and Data Mining Techniques for Cyber Security
Author: Anoop Singhal
Publsiher: Springer Science & Business Media
Total Pages: 159
Release: 2007-04-06
ISBN 10: 0387476539
ISBN 13: 9780387476537
Language: EN, FR, DE, ES & NL

Data Warehousing and Data Mining Techniques for Cyber Security Book Review:

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.

Data Mining with Rattle and R

Data Mining with Rattle and R
Author: Graham Williams
Publsiher: Springer Science & Business Media
Total Pages: 374
Release: 2011-08-04
ISBN 10: 144199890X
ISBN 13: 9781441998903
Language: EN, FR, DE, ES & NL

Data Mining with Rattle and R Book Review:

Data mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of algorithms. Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining. With a focus on the hands-on end-to-end process for data mining, Williams guides the reader through various capabilities of the easy to use, free, and open source Rattle Data Mining Software built on the sophisticated R Statistical Software. The focus on doing data mining rather than just reading about data mining is refreshing. The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment. The reader will learn to rapidly deliver a data mining project using software easily installed for free from the Internet. Coupling Rattle with R delivers a very sophisticated data mining environment with all the power, and more, of the many commercial offerings.

Exploitation of Modern Heuristic Techniques Within a Commercial Data Mining Environment

Exploitation of Modern Heuristic Techniques Within a Commercial Data Mining Environment
Author: J. C. W. Debuse
Publsiher: Unknown
Total Pages: 135
Release: 1997
ISBN 10: 1928374650XXX
ISBN 13: OCLC:60147417
Language: EN, FR, DE, ES & NL

Exploitation of Modern Heuristic Techniques Within a Commercial Data Mining Environment Book Review:

Introduction to Data Mining and Its Applications

Introduction to Data Mining and Its Applications
Author: S. Sumathi,S.N. Sivanandam
Publsiher: Springer Science & Business Media
Total Pages: 828
Release: 2006-09-26
ISBN 10: 3540343504
ISBN 13: 9783540343509
Language: EN, FR, DE, ES & NL

Introduction to Data Mining and Its Applications Book Review:

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 Uses in Commercial Applications

DATA MINING  Uses in Commercial Applications
Author: Vinodani Katiyar,Ina Kapoor
Publsiher: LAP Lambert Academic Publishing
Total Pages: 288
Release: 2015-12-09
ISBN 10: 9783659622311
ISBN 13: 3659622311
Language: EN, FR, DE, ES & NL

DATA MINING Uses in Commercial Applications Book Review:

Although neural networks are applied in a wide range of learning and commercial applications even though this method is not commonly used for data mining tasks. Companies have been collecting data for decades and have build up massive data warehouses and used data mining techniques to extract the value of data but only few practitioners have used neural networks in data mining though this method has proven successful in many situations. The main academic contributions of this study are summarized as follows: (1) implementation of neural networks in analysis of marketing variables; (2) sensitivity analysis and variable reduction through weight analysis; and (3) showing how, by inclusion of Unknown, better results can be obtained from neural networks. Data mining is one of the booming application areas at the moment, and is an area where the operations researcher can find projects with industry. Neural network research is now being driven by industry, as more business problems are attempted and new research challenges emerge.

Data Preparation for Data Mining

Data Preparation for Data Mining
Author: Dorian Pyle
Publsiher: Morgan Kaufmann
Total Pages: 540
Release: 1999-04-05
ISBN 10: 9781558605299
ISBN 13: 1558605290
Language: EN, FR, DE, ES & NL

Data Preparation for Data Mining Book Review:

Data Preparation for Data Mining addresses an issue unfortunately ignored by most authorities on data mining: data preparation. Thanks largely to its perceived difficulty, data preparation has traditionally taken a backseat to the more alluring question of how best to extract meaningful knowledge. But without adequate preparation of your data, the return on the resources invested in mining is certain to be disappointing. Dorian Pyle corrects this imbalance. A twenty-five-year veteran of what has become the data mining industry, Pyle shares his own successful data preparation methodology, offering both a conceptual overview for managers and complete technical details for IT professionals. Apply his techniques and watch your mining efforts pay off-in the form of improved performance, reduced distortion, and more valuable results. On the enclosed CD-ROM, you'll find a suite of programs as C source code and compiled into a command-line-driven toolkit. This code illustrates how the author's techniques can be applied to arrive at an automated preparation solution that works for you. Also included are demonstration versions of three commercial products that help with data preparation, along with sample data with which you can practice and experiment. * Offers in-depth coverage of an essential but largely ignored subject. * Goes far beyond theory, leading you-step by step-through the author's own data preparation techniques. * Provides practical illustrations of the author's methodology using realistic sample data sets. * Includes algorithms you can apply directly to your own project, along with instructions for understanding when automation is possible and when greater intervention is required. * Explains how to identify and correct data problems that may be present in your application. * Prepares miners, helping them head into preparation with a better understanding of data sets and their limitations.

Commercial Data Mining of Criminal Justice System Records

Commercial Data Mining of Criminal Justice System Records
Author: Criminal and Juvenile Justice Information Task Force (Minn.)
Publsiher: Unknown
Total Pages: 48
Release: 2008
ISBN 10: 1928374650XXX
ISBN 13: OCLC:313450522
Language: EN, FR, DE, ES & NL

Commercial Data Mining of Criminal Justice System Records Book Review:

Data Mining

Data Mining
Author: Bhavani Thuraisingham
Publsiher: CRC Press
Total Pages: 288
Release: 1998-12-18
ISBN 10: 9780849318153
ISBN 13: 0849318157
Language: EN, FR, DE, ES & NL

Data Mining Book Review:

Focusing on a data-centric perspective, this book provides a complete overview of data mining: its uses, methods, current technologies, commercial products, and future challenges. Three parts divide Data Mining: Part I describes technologies for data mining - database systems, warehousing, machine learning, visualization, decision support, statistics, parallel processing, and architectural support for data mining Part II presents tools and techniques - getting the data ready, carrying out the mining, pruning the results, evaluating outcomes, defining specific approaches, examining a specific technique based on logic programming, and citing literature and vendors for up-to-date information Part III examines emerging trends - mining distributed and heterogeneous data sources; multimedia data, such as text, images, video; mining data on the World Wide Web; metadata aspects of mining; and privacy issues. This self-contained book also contains two appendices providing exceptional information on technologies, such as data management, and artificial intelligence. Is there a need for mining? Do you have the right tools? Do you have the people to do the work? Do you have sufficient funds allocated to the project? All these answers must be answered before embarking on a project. Data Mining provides singular guidance on appropriate applications for specific techniques as well as thoroughly assesses valuable product information.

Real World Data Mining Applications

Real World Data Mining Applications
Author: Mahmoud Abou-Nasr,Stefan Lessmann,Robert Stahlbock,Gary M. Weiss
Publsiher: Springer
Total Pages: 418
Release: 2014-11-13
ISBN 10: 3319078127
ISBN 13: 9783319078120
Language: EN, FR, DE, ES & NL

Real World Data Mining Applications Book Review:

Data mining applications range from commercial to social domains, with novel applications appearing swiftly; for example, within the context of social networks. The expanding application sphere and social reach of advanced data mining raise pertinent issues of privacy and security. Present-day data mining is a progressive multidisciplinary endeavor. This inter- and multidisciplinary approach is well reflected within the field of information systems. The information systems research addresses software and hardware requirements for supporting computationally and data-intensive applications. Furthermore, it encompasses analyzing system and data aspects, and all manual or automated activities. In that respect, research at the interface of information systems and data mining has significant potential to produce actionable knowledge vital for corporate decision-making. The aim of the proposed volume is to provide a balanced treatment of the latest advances and developments in data mining; in particular, exploring synergies at the intersection with information systems. It will serve as a platform for academics and practitioners to highlight their recent achievements and reveal potential opportunities in the field. Thanks to its multidisciplinary nature, the volume is expected to become a vital resource for a broad readership ranging from students, throughout engineers and developers, to researchers and academics.

Monetising Data

Monetising Data
Author: Andrea Ahlemeyer-Stubbe,Shirley Coleman
Publsiher: John Wiley & Sons
Total Pages: 384
Release: 2018-02-01
ISBN 10: 1119125146
ISBN 13: 9781119125143
Language: EN, FR, DE, ES & NL

Monetising Data Book Review:

Practical guide for deriving insight and commercial gain from data Monetising Data offers a practical guide for anyone working with commercial data but lacking deep knowledge of statistics or data mining. The authors — noted experts in the field — show how to generate extra benefit from data already collected and how to use it to solve business problems. In accessible terms, the book details ways to extract data to enhance business practices and offers information on important topics such as data handling and management, statistical methods, graphics and business issues. The text presents a wide range of illustrative case studies and examples to demonstrate how to adapt the ideas towards monetisation, no matter the size or type of organisation. The authors explain on a general level how data is cleaned and matched between data sets and how we learn from data analytics to address vital business issues. The book clearly shows how to analyse and organise data to identify people and follow and interact with them through the customer lifecycle. Monetising Data is an important resource: Focuses on different business scenarios and opportunities to turn data into value Gives an overview on how to store, manage and maintain data Presents mechanisms for using knowledge from data analytics to improve the business and increase profits Includes practical suggestions for identifying business issues from the data Written for everyone engaged in improving the performance of a company, including managers and students, Monetising Data is an essential guide for understanding and using data to enrich business practice.

DATA MINING The CRISP DM METHODOLOGY The CLEM language and IBM SPSS MODELER

DATA MINING  The CRISP DM METHODOLOGY  The CLEM language and IBM SPSS MODELER
Author: César Pérez López
Publsiher: Lulu Press, Inc
Total Pages: 135
Release: 2021-03-28
ISBN 10: 1008981656
ISBN 13: 9781008981652
Language: EN, FR, DE, ES & NL

DATA MINING The CRISP DM METHODOLOGY The CLEM language and IBM SPSS MODELER Book Review:

This book describes the CRISP-DM modelling process for data mining. SPSS (then ISL and now IBM-SPSS) had been providing Data Mining based services since 1990 and had launched the first commercial Data Mining tool Clementine in 1994. Now the principal product that implements the CRISP-DM Methodology is IBM SPSS Modeler. This book describes also the Control Language for Expression Manipulation (CLEM), which is a powerful tool used to analyze and manipulate the data used in IBM SPSS Modeler streams. You can use CLEM within nodes to perform tasks ranging from evaluating conditions or deriving values to inserting data into reports. CLEM expressions consist of values, field names, operators, and functions. Using the correct syntax, you can create a wide variety of powerful data operations. IBM SPSS Modeler is an integrated data mining tool that includes several data sources (ASCII, XLS, ODBC, etc.), a visual interface based on data processes/flows (streams), different data mining tools (correlation, association rules, regression, segmentation, classification, neural networks, decision rules and trees, etc.), data manipulation (pick & mix, sampling, combination and separation, etc.), model combination, data visualisation, model export to different languages (C, SPSS, SAS, etc.), integrated data export to other programs (XLS) and report generation.

Data Mining and Statistics for Decision Making

Data Mining and Statistics for Decision Making
Author: Stéphane Tufféry
Publsiher: John Wiley & Sons
Total Pages: 716
Release: 2011-03-23
ISBN 10: 9780470979280
ISBN 13: 0470979283
Language: EN, FR, DE, ES & NL

Data Mining and Statistics for Decision Making Book Review:

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.

Predictive Data Mining

Predictive Data Mining
Author: Sholom M. Weiss,Nitin Indurkhya
Publsiher: Morgan Kaufmann
Total Pages: 228
Release: 1998
ISBN 10: 9781558604032
ISBN 13: 1558604030
Language: EN, FR, DE, ES & NL

Predictive Data Mining Book Review:

This book is the first technical guide to provide a complete, generalized road map for developing data-mining applications, together with advice on performing these large-scale, open-ended analyses for real-world data warehouses.

Data Mining Techniques

Data Mining Techniques
Author: Michael J. A. Berry,Gordon S. Linoff
Publsiher: John Wiley & Sons
Total Pages: 643
Release: 2004-04-09
ISBN 10: 0471470643
ISBN 13: 9780471470649
Language: EN, FR, DE, ES & NL

Data Mining Techniques Book Review:

Many companies have invested in building large databases and data warehouses capable of storing vast amounts of information. This book offers business, sales and marketing managers a practical guide to accessing such information.

Data Mining

Data Mining
Author: Linda D. Koontz
Publsiher: DIANE Publishing
Total Pages: 76
Release: 2005-11
ISBN 10: 9781422302668
ISBN 13: 1422302660
Language: EN, FR, DE, ES & NL

Data Mining Book Review:

Data mining -- a technique for extracting knowledge from large volumes of data -- is being used increasingly by the gov't. & by the private sector. Many fed. data mining efforts involve the use of personal information, which can originate from gov't. sources as well as private sector organizations. The federal government's increased use of data mining since the terrorist attacks of Sept. 11, 2001, has raised public & congressional concerns. This report describes the characteristics of 5 federal data mining efforts & determines whether agencies are providing adequate privacy & security protection for the information systems used in the efforts & for individuals potentially affected by these data mining efforts. Includes recommendations. Charts & tables.

Data Mining Techniques and Applications

Data Mining Techniques and Applications
Author: Hongbo Du
Publsiher: Unknown
Total Pages: 315
Release: 2010
ISBN 10: 9781844808915
ISBN 13: 1844808912
Language: EN, FR, DE, ES & NL

Data Mining Techniques and Applications Book Review:

This concise and approachable introduction to data mining selects a mixture of data mining techniques originating from statistics, machine learning and databases, and presents them in an algorithmic approach. Aimed primarily at undergraduate readers, it presents not only the fundamental principles and concepts of the subject in an easy-to-understand way, but also hands on, practical instruction on data mining techniques, that readers can put into practice as they go along using the freely downloadable Weka toolkit. Author Hongbo Du shares his years of commercial, as well as research-based, experience in the field through extensive examples and real-world case studies, highlighting how data mining solutions provided by software tools are used in practical problem solving. Covering not only traditional areas of data mining such as association, clustering and classification, this text also explains topics such as data warehousing, online-analytic processing, and text mining.