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Author: Carlos A. Mota Soares
Publisher: IOS Press
Published: 2008
Total Pages: 156
ISBN-13: 1586038907
DOWNLOAD EBOOKContains 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.
Author: Boris Kovalerchuk
Publisher: Springer Science & Business Media
Published: 2005-12-11
Total Pages: 323
ISBN-13: 0306470187
DOWNLOAD EBOOKData 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: Galit Shmueli
Publisher: John Wiley & Sons
Published: 2019-10-14
Total Pages: 608
ISBN-13: 111954985X
DOWNLOAD EBOOKData 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
Author: Cheng Few Lee
Publisher: World Scientific
Published: 2020-07-30
Total Pages: 5053
ISBN-13: 9811202400
DOWNLOAD EBOOKThis four-volume handbook covers important concepts and tools used in the fields of financial econometrics, mathematics, statistics, and machine learning. Econometric methods have been applied in asset pricing, corporate finance, international finance, options and futures, risk management, and in stress testing for financial institutions. This handbook discusses a variety of econometric methods, including single equation multiple regression, simultaneous equation regression, and panel data analysis, among others. It also covers statistical distributions, such as the binomial and log normal distributions, in light of their applications to portfolio theory and asset management in addition to their use in research regarding options and futures contracts.In both theory and methodology, we need to rely upon mathematics, which includes linear algebra, geometry, differential equations, Stochastic differential equation (Ito calculus), optimization, constrained optimization, and others. These forms of mathematics have been used to derive capital market line, security market line (capital asset pricing model), option pricing model, portfolio analysis, and others.In recent times, an increased importance has been given to computer technology in financial research. Different computer languages and programming techniques are important tools for empirical research in finance. Hence, simulation, machine learning, big data, and financial payments are explored in this handbook.Led by Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues based on his years of academic and industry experience.
Author: Carlos A. Mota Soares
Publisher: IOS Press
Published: 2010
Total Pages: 196
ISBN-13: 1607506327
DOWNLOAD EBOOKData 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.
Author: Daniel S. Putler
Publisher: CRC Press
Published: 2012-05-07
Total Pages: 314
ISBN-13: 146650398X
DOWNLOAD EBOOKCustomer 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 tex
Author: Jagdish Chandra Patni
Publisher:
Published: 2017-09-12
Total Pages: 56
ISBN-13: 9783668519633
DOWNLOAD EBOOKAcademic Paper from the year 2017 in the subject Computer Science - General, grade: 5, University of Petroleum and Energy Studies, language: English, abstract: This paper utilizes the distinctive mining techniques as an answer for business needs. It presents Finance, Budgeting and Investments as the principle working ground for the data mining algorithms actualized. With the increment of monetary globalization and development of information technology, financial data are being produced and gathered at an extraordinary pace. Thus, there has been a basic requirement for automated ways to deal with compelling and proficient usage of gigantic measure of data to support companies and people in doing the Business. Data mining is turning out to be strategically imperative region for some business associations including financial sector. Data mining helps the companies to search for hidden example in a gathering and find obscure relationship in the data. Financial Analysis alludes to the assessment of a business to manage the arranging, budgeting, observing, forecasting, and enhancing of every financial point of interest inside of an association. The task concentrates on comprehension the association's financial health as a major part of reacting to today's inexorably stringent financial reporting prerequisites. It exhibits the capacity of the data mining to robotize the procedure of looking the boundless customer's connected data to discover patterns that are great indicators of the practices of the customer. This will cover the analysis of: Profit arranging, Cash flow analysis, Investment decisions and risk analysis, Dividend Policies and Portfolio Analysis through algorithms like Apriori, Naivebayes, Prediction algorithm and so forth. Along these lines this Data mining arrangement actualizes advanced data analysis techniques utilized by companies for discovering startling patterns extricated from tremendous measures of data, patterns that offer applicable knowledge for
Author: Kenneth D. Lawrence
Publisher: IAP
Published: 2015-07-01
Total Pages: 237
ISBN-13: 1681230895
DOWNLOAD EBOOKThe series, Contemporary Perspectives on Data Mining, is composed of blind refereed scholarly research methods and applications of data mining. This series will be targeted both at the academic community, as well as the business practitioner. Data mining seeks to discover knowledge from vast amounts of data with the use of statistical and mathematical techniques. The knowledge is extracted from this data by examining the patterns of the data, whether they be associations of groups or things, predictions, sequential relationships between time order events or natural groups. Data mining applications are in marketing (customer loyalty, identifying profitable customers, instore promotions, e-commerce populations); in business (teaching data mining, efficiency of the Chinese automobile industry, moderate asset allocation funds); and techniques (veterinary predictive models, data integrity in the cloud, irregular pattern detection in a mobility network and road safety modeling.)
Author: Barry de Ville
Publisher: Elsevier
Published: 2001-05-17
Total Pages: 338
ISBN-13: 0080491847
DOWNLOAD EBOOKMicrosoft Data Mining approaches data mining from the particular perspective of IT professionals using Microsoft data management technologies. The author explains the new data mining capabilities in Microsoft's SQL Server 2000 database, Commerce Server, and other products, details the Microsoft OLE DB for Data Mining standard, and gives readers best practices for using all of them. The book bridges the previously specialized field of data mining with the new technologies and methods that are quickly making it an important mainstream tool for companies of all sizes.Data mining refers to a set of technologies and techniques by which IT professionals search large databases of information (such as those contained by SQL Server) for patterns and trends. Traditionally important in finance, telecommunication, and other information-intensive fields, data mining increasingly helps companies better understand and serve their customers by revealing buying patterns and related interests. It is becoming a foundation for e-commerce and knowledge management. - Unique book on a hot data management topic - Part of Digital Press's SQL Server and data mining clusters - Author is an expert on both traditional and Microsoft data mining technologies
Author: Bhavani Thuraisingham
Publisher: CRC Press
Published: 2003-06-26
Total Pages: 542
ISBN-13: 0203499514
DOWNLOAD EBOOKThe explosion of Web-based data has created a demand among executives and technologists for methods to identify, gather, analyze, and utilize data that may be of value to corporations and organizations. The emergence of data mining, and the larger field of Web mining, has businesses lost within a confusing maze of mechanisms and strategies for obta
