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Author: Bruce Ratner
Publisher: CRC Press
ISBN: 1466551216
Category : Business & Economics
Languages : en
Pages : 544
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Book Description
The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, contained 17 chapters of innovative and practical statistical data mining techniques. In this second edition, renamed to reflect the increased coverage of machine-learning data mining techniques, the author has completely revised, reorganized, and repositioned the original chapters and produced 14 new chapters of creative and useful machine-learning data mining techniques. In sum, the 31 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. The statistical data mining methods effectively consider big data for identifying structures (variables) with the appropriate predictive power in order to yield reliable and robust large-scale statistical models and analyses. In contrast, the author's own GenIQ Model provides machine-learning solutions to common and virtually unapproachable statistical problems. GenIQ makes this possible — its utilitarian data mining features start where statistical data mining stops. This book contains essays offering detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. They address each methodology and assign its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.
Author: Bruce Ratner
Publisher: CRC Press
ISBN: 1466551216
Category : Business & Economics
Languages : en
Pages : 544
Get Book Here
Book Description
The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, contained 17 chapters of innovative and practical statistical data mining techniques. In this second edition, renamed to reflect the increased coverage of machine-learning data mining techniques, the author has completely revised, reorganized, and repositioned the original chapters and produced 14 new chapters of creative and useful machine-learning data mining techniques. In sum, the 31 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. The statistical data mining methods effectively consider big data for identifying structures (variables) with the appropriate predictive power in order to yield reliable and robust large-scale statistical models and analyses. In contrast, the author's own GenIQ Model provides machine-learning solutions to common and virtually unapproachable statistical problems. GenIQ makes this possible — its utilitarian data mining features start where statistical data mining stops. This book contains essays offering detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. They address each methodology and assign its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.
Author: Papadakis, Stylianos
Publisher: IGI Global
ISBN: 179984806X
Category : Business & Economics
Languages : en
Pages : 270
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Book Description
The prediction of the valuation of the “quality” of firm accounting disclosure is an emerging economic problem that has not been adequately analyzed in the relevant economic literature. While there are a plethora of machine learning methods and algorithms that have been implemented in recent years in the field of economics that aim at creating predictive models for detecting business failure, only a small amount of literature is provided towards the prediction of the “actual” financial performance of the business activity. Machine Learning Applications for Accounting Disclosure and Fraud Detection is a crucial reference work that uses machine learning techniques in accounting disclosure and identifies methodological aspects revealing the deployment of fraudulent behavior and fraud detection in the corporate environment. The book applies machine learning models to identify “quality” characteristics in corporate accounting disclosure, proposing specific tools for detecting core business fraud characteristics. Covering topics that include data mining; fraud governance, detection, and prevention; and internal auditing, this book is essential for accountants, auditors, managers, fraud detection experts, forensic accountants, financial accountants, IT specialists, corporate finance experts, business analysts, academicians, researchers, and students.
Author: Lars Hornuf
Publisher: Springer Nature
ISBN: 3031040635
Category : Business & Economics
Languages : en
Pages : 218
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Book Description
This book focuses on traditional fields of business studies and economics and how digitalization has affected them. It provides an overview about the lessons learned from academic research and highlights implications for practitioners. Digitalization has not only changed the ways business administration and economics are taught, but also the substance at the core of the two disciplines. Chapters from expert contributors define and carefully evaluate the developments that have occurred over the last decades. The authors further provide an assessment of how industry branches have adapted and in which form regulators have engaged. Attention is given to the theoretical and empirical findings from recent scholarly literature. Furthermore, the authors provide some novel insights from their own research at the University of Bremen. This book appeals to business administration, economics, and entrepreneurship scholars and practitioners alike.
Author: Boris Kovalerchuk
Publisher: Springer Science & Business Media
ISBN: 0306470187
Category : Computers
Languages : en
Pages : 323
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Book Description
Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these approaches to financial data mining. The book focuses specifically on relational data mining (RDM), which is a learning method able to learn more expressive rules than other symbolic approaches. RDM is thus better suited for financial mining, because it is able to make greater use of underlying domain knowledge. Relational data mining also has a better ability to explain the discovered rules - an ability critical for avoiding spurious patterns which inevitably arise when the number of variables examined is very large. The earlier algorithms for relational data mining, also known as inductive logic programming (ILP), suffer from a relative computational inefficiency and have rather limited tools for processing numerical data. Data Mining in Finance introduces a new approach, combining relational data mining with the analysis of statistical significance of discovered rules. This reduces the search space and speeds up the algorithms. The book also presents interactive and fuzzy-logic tools for `mining' the knowledge from the experts, further reducing the search space. Data Mining in Finance contains a number of practical examples of forecasting S&P 500, exchange rates, stock directions, and rating stocks for portfolio, allowing interested readers to start building their own models. This book is an excellent reference for researchers and professionals in the fields of artificial intelligence, machine learning, data mining, knowledge discovery, and applied mathematics.
Author: Dorian Pyle
Publisher: Morgan Kaufmann
ISBN: 9781558605299
Category : Computers
Languages : en
Pages : 566
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Book Description
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.
Author: Bahaaeddin Alareeni
Publisher: Springer Nature
ISBN: 3030692213
Category : Technology & Engineering
Languages : en
Pages : 2139
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Book Description
This book introduces the students, researchers and practitioners into the subject and enabling technologies and applications pertaining to of technology, entrepreneurship and business development through research articles, case studies etc. It is primarily intended for academic purposes for learners of computer Science, management, accounting and information systems disciplines, economics,- entrepreneurship. Publishing chapters in the book is new innovative idea to spread the book in the Middle East and Arab countries and make the book achieve more sales. As many students in all levels, graduates and undergraduates in addition to research, professionals are not able to get sufficient resources because of the language concern.
Author:
Publisher: DIANE Publishing
ISBN: 1428944737
Category :
Languages : en
Pages : 272
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Book Description
Author: Volodymyr Babich
Publisher: Springer Nature
ISBN: 3030757293
Category : Business & Economics
Languages : en
Pages : 304
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Book Description
This book examines the challenges and opportunities arising from an assortment of technologies as they relate to Operations Management and Finance. The book contains primers on operations, finance, and their interface. After that, each section contains chapters in the categories of theory, applications, case studies, and teaching resources. These technologies and business models include Big Data and Analytics, Artificial Intelligence, Machine Learning, Blockchain, IoT, 3D printing, sharing platforms, crowdfunding, and crowdsourcing. The balance between theory, applications, and teaching materials make this book an interesting read for academics and practitioners in operations and finance who are curious about the role of new technologies. The book is an attractive choice for PhD-level courses and for self-study.
Author: Gerard M. Zack
Publisher: John Wiley & Sons
ISBN: 1118421477
Category : Business & Economics
Languages : en
Pages : 320
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Book Description
Valuable guidance for staying one step ahead of financial statement fraud Financial statement fraud is one of the most costly types of fraud and can have a direct financial impact on businesses and individuals, as well as harm investor confidence in the markets. While publications exist on financial statement fraud and roles and responsibilities within companies, there is a need for a practical guide on the different schemes that are used and detection guidance for these schemes. Financial Statement Fraud: Strategies for Detection and Investigation fills that need. Describes every major and emerging type of financial statement fraud, using real-life cases to illustrate the schemes Explains the underlying accounting principles, citing both U.S. GAAP and IFRS that are violated when fraud is perpetrated Provides numerous ratios, red flags, and other techniques useful in detecting financial statement fraud schemes Accompanying website provides full-text copies of documents filed in connection with the cases that are cited as examples in the book, allowing the reader to explore details of each case further Straightforward and insightful, Financial Statement Fraud provides comprehensive coverage on the different ways financial statement fraud is perpetrated, including those that capitalize on the most recent accounting standards developments, such as fair value issues.
Author: Gerard M. Zack
Publisher: John Wiley & Sons
ISBN: 1118301552
Category : Business & Economics
Languages : en
Pages : 320
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Book Description
Valuable guidance for staying one step ahead of financial statement fraud Financial statement fraud is one of the most costly types of fraud and can have a direct financial impact on businesses and individuals, as well as harm investor confidence in the markets. While publications exist on financial statement fraud and roles and responsibilities within companies, there is a need for a practical guide on the different schemes that are used and detection guidance for these schemes. Financial Statement Fraud: Strategies for Detection and Investigation fills that need. Describes every major and emerging type of financial statement fraud, using real-life cases to illustrate the schemes Explains the underlying accounting principles, citing both U.S. GAAP and IFRS that are violated when fraud is perpetrated Provides numerous ratios, red flags, and other techniques useful in detecting financial statement fraud schemes Accompanying website provides full-text copies of documents filed in connection with the cases that are cited as examples in the book, allowing the reader to explore details of each case further Straightforward and insightful, Financial Statement Fraud provides comprehensive coverage on the different ways financial statement fraud is perpetrated, including those that capitalize on the most recent accounting standards developments, such as fair value issues.
