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Commercial Data Mining

Commercial Data Mining
  • Author : David Nettleton
  • Publisher :Unknown
  • Release Date :2014-01-29
  • Total pages :304
  • ISBN : 9780124166585
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Summary : 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

Exam Prep for: Commercial Data Mining; Processing, Analysis ...

Exam Prep for: Commercial Data Mining; Processing, Analysis ...
  • Author : Anonim
  • Publisher :Unknown
  • Release Date :2021
  • Total pages :229
  • ISBN : 1230987654XX
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Summary :

Data Mining for Business Analytics

Data Mining for Business Analytics
  • Author : Galit Shmueli,Peter C. Bruce,Peter Gedeck,Nitin R. Patel
  • Publisher :Unknown
  • Release Date :2019-10-14
  • Total pages :608
  • ISBN : 9781119549857
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Summary : Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” —Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R

Data Warehousing and Data Mining Techniques for Cyber Security

Data Warehousing and Data Mining Techniques for Cyber Security
  • Author : Anoop Singhal
  • Publisher :Unknown
  • Release Date :2007-04-06
  • Total pages :159
  • ISBN : 9780387476537
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Summary : 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.

Principles of Data Mining

Principles of Data Mining
  • Author : Max Bramer
  • Publisher :Unknown
  • Release Date :2016-11-09
  • Total pages :526
  • ISBN : 9781447173076
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Summary : 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.

Data Mining

Data Mining
  • Author : Bhavani Thuraisingham
  • Publisher :Unknown
  • Release Date :2014-01-23
  • Total pages :288
  • ISBN : 9781482252507
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Summary : 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.

Data Mining and Statistics for Decision Making

Data Mining and Statistics for Decision Making
  • Author : Stéphane Tufféry
  • Publisher :Unknown
  • Release Date :2011-03-23
  • Total pages :716
  • ISBN : 0470979283
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Summary : 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.

Introduction to Data Mining and Its Applications

Introduction to Data Mining and Its Applications
  • Author : S. Sumathi,S.N. Sivanandam
  • Publisher :Unknown
  • Release Date :2006-09-26
  • Total pages :828
  • ISBN : 9783540343509
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Summary : 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.

Visual Data Mining

Visual Data Mining
  • Author : Tom Soukup,Ian Davidson
  • Publisher :Unknown
  • Release Date :2002-09-18
  • Total pages :416
  • ISBN : 9780471271383
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Summary : Marketing analysts use data mining techniques to gain a reliable understanding of customer buying habits and then use that information to develop new marketing campaigns and products. Visual mining tools introduce a world of possibilities to a much broader and non-technical audience to help them solve common business problems. Explains how to select the appropriate data sets for analysis, transform the data sets into usable formats, and verify that the sets are error-free Reviews how to choose the right model for the specific type of analysis project, how to analyze the model, and present the results for decision making Shows how to solve numerous business problems by applying various tools and techniques Companion Web site offers links to data visualization and visual data mining tools, and real-world success stories using visual data mining

Data mining

Data mining
  • Author : United States,United States. Congress. House. Committee on Government Reform. Subcommittee on Technology, Information Policy, Intergovernmental Relations, and the Census
  • Publisher :Unknown
  • Release Date :2003
  • Total pages :100
  • ISBN : PURD:32754075579148
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Summary :

Data Mining with Rattle and R

Data Mining with Rattle and R
  • Author : Graham Williams
  • Publisher :Unknown
  • Release Date :2011-08-04
  • Total pages :374
  • ISBN : 9781441998903
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Summary : 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.

Data Mining Techniques and Applications

Data Mining Techniques and Applications
  • Author : Hongbo Du
  • Publisher :Unknown
  • Release Date :2010
  • Total pages :315
  • ISBN : 1844808912
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Summary : 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.

Data Mining Models, Second Edition

Data Mining Models, Second Edition
  • Author : David L. Olson
  • Publisher :Unknown
  • Release Date :2018-04-25
  • Total pages :182
  • ISBN : 9781948580502
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Summary : Data mining has become the fastest growing topic of interest in business programs in the past decade. This book is intended to describe the benefits of data mining in business, the process and typical business applications, the workings of basic data mining models, and demonstrate each with widely available free software. The book focuses on demonstrating common business data mining applications. It provides exposure to the data mining process, to include problem identification, data management, and available modeling tools. The book takes the approach of demonstrating typical business data sets with open source software. KNIME is a very easy-to-use tool, and is used as the primary means of demonstration. R is much more powerful and is a commercially viable data mining tool. We also demonstrate WEKA, which is a highly useful academic software, although it is difficult to manipulate test sets and new cases, making it problematic for commercial use.

Customer and Business Analytics

Customer and Business Analytics
  • Author : Daniel S. Putler,Robert E. Krider
  • Publisher :Unknown
  • Release Date :2015-09-15
  • Total pages :315
  • ISBN : 9781498759700
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Summary : Customer and Business Analytics: Applied Data Mining for Business Decision Making Using R explains and demonstrates, via the accompanying open-source software, how advanced analytical tools can address various business problems. It also gives insight into some of the challenges faced when deploying these tools. Extensively classroom-tested, the text is ideal for students in customer and business analytics or applied data mining as well as professionals in small- to medium-sized organizations. The book offers an intuitive understanding of how different analytics algorithms work. Where necessary, the authors explain the underlying mathematics in an accessible manner. Each technique presented includes a detailed tutorial that enables hands-on experience with real data. The authors also discuss issues often encountered in applied data mining projects and present the CRISP-DM process model as a practical framework for organizing these projects. Showing how data mining can improve the performance of organizations, this book and its R-based software provide the skills and tools needed to successfully develop advanced analytics capabilities.

Data Mining for Scientific and Engineering Applications

Data Mining for Scientific and Engineering Applications
  • Author : R.L. Grossman,C. Kamath,V. Kumar,P. Kegelmeyer,R. Namburu
  • Publisher :Unknown
  • Release Date :2001-10-31
  • Total pages :605
  • ISBN : 1402000332
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Summary : Advances in technology are making massive data sets common in many scientific disciplines, such as astronomy, medical imaging, bio-informatics, combinatorial chemistry, remote sensing, and physics. To find useful information in these data sets, scientists and engineers are turning to data mining techniques. This book is a collection of papers based on the first two in a series of workshops on mining scientific datasets. It illustrates the diversity of problems and application areas that can benefit from data mining, as well as the issues and challenges that differentiate scientific data mining from its commercial counterpart. While the focus of the book is on mining scientific data, the work is of broader interest as many of the techniques can be applied equally well to data arising in business and web applications. Audience: This work would be an excellent text for students and researchers who are familiar with the basic principles of data mining and want to learn more about the application of data mining to their problem in science or engineering.

Mining Your Own Business

Mining Your Own Business
  • Author : Jeff Deal,Gerhard Pilcher
  • Publisher :Unknown
  • Release Date :2016-09-19
  • Total pages :229
  • ISBN : 0996712100
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Summary : Practical guide for organization leaders, top-level executives. Industry experts explain in clear, understandable English. What data mining and predictive analytics are

Data Preparation for Data Mining

Data Preparation for Data Mining
  • Author : Dorian Pyle
  • Publisher :Unknown
  • Release Date :1999-04-05
  • Total pages :540
  • ISBN : 1558605290
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Summary : 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 Science for Business

Data Science for Business
  • Author : Foster Provost,Tom Fawcett
  • Publisher :Unknown
  • Release Date :2013-07-27
  • Total pages :414
  • ISBN : 9781449374280
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Summary : Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates

A Practical Guide to Data Mining for Business and Industry

A Practical Guide to Data Mining for Business and Industry
  • Author : Andrea Ahlemeyer-Stubbe,Shirley Coleman
  • Publisher :Unknown
  • Release Date :2014-03-31
  • Total pages :328
  • ISBN : 9781118763377
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Summary : 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.

Predictive Data Mining

Predictive Data Mining
  • Author : Sholom M. Weiss,Nitin Indurkhya
  • Publisher :Unknown
  • Release Date :1998
  • Total pages :228
  • ISBN : 1558604030
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Summary : This book presents a unified view of data mining, drawing from statistics, machine learning, and databases and focuses on the preparation of data and the development of an overall problem-solving strategy. It will interest researchers, programmers, and developers in knowledge discovery and data mining in the disciplines of AI, software engineering, and databases.

Predictive Analytics for Marketers

Predictive Analytics for Marketers
  • Author : Barry Leventhal
  • Publisher :Unknown
  • Release Date :2018-02-03
  • Total pages :272
  • ISBN : 9780749479947
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Summary : Predictive analytics has revolutionized marketing practice. It involves using many techniques from data mining, statistics, modelling, machine learning and artificial intelligence, to analyse current data and make predictions about unknown future events. In business terms, this enables companies to forecast consumer behaviour and much more. Predictive Analytics for Marketers will guide marketing professionals on how to apply predictive analytical tools to streamline business practices. Including comprehensive coverage of an array of predictive analytic tools and techniques, this book enables readers to harness patterns from past data, to make accurate and useful predictions that can be converted to business success. Truly global in its approach, the insights these techniques offer can be used to manage resources more effectively across all industries and sectors. Written in clear, non-technical language, Predictive Analytics for Marketers contains case studies from the author's more than 25 years of experience and articles from guest contributors, demonstrating how predictive analytics has been used to successfully achieve a range of business purposes.