Artificial Intelligence And Data Mining For Mergers And Acquisitions
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Artificial Intelligence And Data Mining For Mergers And Acquisitions
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Author : Debasis Chanda
language : en
Publisher:
Release Date : 2021
Artificial Intelligence And Data Mining For Mergers And Acquisitions written by Debasis Chanda and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Artificial Intelligence And Data Mining For Mergers And Acquisitions
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Author : Debasis Chanda
language : en
Publisher: CRC Press
Release Date : 2021-03-18
Artificial Intelligence And Data Mining For Mergers And Acquisitions written by Debasis Chanda and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-18 with Business & Economics categories.
The goal of this book is to present a modeling framework for the Virtual Organization that is focused on process composition. This framework uses Predicate Calculus Knowledge Bases. Petri Net-based modeling is also discussed. In this context, a Data Mining model is proposed, using a fuzzy mathematical approach, aiming to discover knowledge. A Knowledge-Based framework has been proposed in order to present an all-inclusive knowledge store for static and dynamic properties. Toward this direction, a Knowledge Base is created, and inferences are arrived at. This book features an advisory tool for Mergers and Acquisitions of Organizations using the Fuzzy Data Mining Framework and highlights the novelty of a Knowledge-Based Service-Oriented Architecture approach and development of an Enterprise Architectural model using AI that serves a wide audience. Students of Strategic Management in business schools and postgraduate programs in technology institutes seeking application areas of AI and Data Mining, as well as business/technology professionals in organizations aiming to create value through Mergers and Acquisitions and elsewhere, will benefit from the reading of this book.
Data Mining In Finance
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Author : Boris Kovalerchuk
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-12-11
Data Mining In Finance written by Boris Kovalerchuk and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-12-11 with Computers categories.
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.
Data Mining And Knowledge Discovery Approaches Based On Rule Induction Techniques
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Author : Evangelos Triantaphyllou
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-09-10
Data Mining And Knowledge Discovery Approaches Based On Rule Induction Techniques written by Evangelos Triantaphyllou and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-09-10 with Computers categories.
This book outlines the core theory and practice of data mining and knowledge discovery (DM & KD) examining theoretical foundations for various methods, and presenting an array of examples, many drawn from real-life applications. Most theoretical developments are accompanied by extensive empirical analysis, offering a deep insight into both theoretical and practical aspects of the subject. The book presents the combined research experiences of 40 expert contributors of world renown.
Advances In Data Mining
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Author : Petra Perner
language : en
Publisher: Springer Science & Business Media
Release Date : 2002-08-21
Advances In Data Mining written by Petra Perner and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-08-21 with Business & Economics categories.
This book presents six thoroughly reviewed and revised full papers describing selected projects on data mining. Three papers deal with data mining and e-commerce, focusing on sequence rule analysis, association rule mining and knowledge discovery in databases, and intelligent e-marketing with Web mining. One paper is devoted to experience management and process learning. The last two papers report on medical applications, namely on genomic data processing and on case-based reasoning for prognosis of influenza.
Machine Learning And Its Applications
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Author : Georgios Paliouras
language : en
Publisher: Springer Science & Business Media
Release Date : 2001-08-01
Machine Learning And Its Applications written by Georgios Paliouras and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-08-01 with Computers categories.
In recent years machine learning has made its way from artificial intelligence into areas of administration, commerce, and industry. Data mining is perhaps the most widely known demonstration of this migration, complemented by less publicized applications of machine learning like adaptive systems in industry, financial prediction, medical diagnosis and the construction of user profiles for Web browsers. This book presents the capabilities of machine learning methods and ideas on how these methods could be used to solve real-world problems. The first ten chapters assess the current state of the art of machine learning, from symbolic concept learning and conceptual clustering to case-based reasoning, neural networks, and genetic algorithms. The second part introduces the reader to innovative applications of ML techniques in fields such as data mining, knowledge discovery, human language technology, user modeling, data analysis, discovery science, agent technology, finance, etc.
Data Mining
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Author : Bhavani Thuraisingham
language : en
Publisher: CRC Press
Release Date : 2014-01-23
Data Mining written by Bhavani Thuraisingham and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-23 with Computers categories.
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 For Business Applications
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Author : Longbing Cao
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-10-03
Data Mining For Business Applications written by Longbing Cao and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-10-03 with Computers categories.
Data Mining for Business Applications presents the state-of-the-art research and development outcomes on methodologies, techniques, approaches and successful applications in the area. The contributions mark a paradigm shift from “data-centered pattern mining” to “domain driven actionable knowledge discovery” for next-generation KDD research and applications. The contents identify how KDD techniques can better contribute to critical domain problems in theory and practice, and strengthen business intelligence in complex enterprise applications. The volume also explores challenges and directions for future research and development in the dialogue between academia and business.
Knowledge Discovery For Business Information Systems
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Author : Witold Abramowicz
language : en
Publisher: Springer Science & Business Media
Release Date : 2001
Knowledge Discovery For Business Information Systems written by Witold Abramowicz and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Business & Economics categories.
Knowledge discovery (KDD) and Data Mining (DM) is a new, multidisciplinary field focusing on the process of information discovery from large volumes of data. The field combines such areas as database concepts and theory, machine learning, pattern recognition, and artificial intelligence.
Recent Advances In Data Mining Of Enterprise Data
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Author : T. Warren Liao
language : en
Publisher: World Scientific
Release Date : 2008-01-15
Recent Advances In Data Mining Of Enterprise Data written by T. Warren Liao and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-01-15 with Business & Economics categories.
The main goal of the new field of data mining is the analysis of large and complex datasets. Some very important datasets may be derived from business and industrial activities. This kind of data is known as OC enterprise dataOCO. The common characteristic of such datasets is that the analyst wishes to analyze them for the purpose of designing a more cost-effective strategy for optimizing some type of performance measure, such as reducing production time, improving quality, eliminating wastes, or maximizing profit. Data in this category may describe different scheduling scenarios in a manufacturing environment, quality control of some process, fault diagnosis in the operation of a machine or process, risk analysis when issuing credit to applicants, management of supply chains in a manufacturing system, or data for business related decision-making. Sample Chapter(s). Foreword (37 KB). Chapter 1: Enterprise Data Mining: A Review and Research Directions (655 KB). Contents: Enterprise Data Mining: A Review and Research Directions (T W Liao); Application and Comparison of Classification Techniques in Controlling Credit Risk (L Yu et al.); Predictive Classification with Imbalanced Enterprise Data (S Daskalaki et al.); Data Mining Applications of Process Platform Formation for High Variety Production (J Jiao & L Zhang); Multivariate Control Charts from a Data Mining Perspective (G C Porzio & G Ragozini); Maintenance Planning Using Enterprise Data Mining (L P Khoo et al.); Mining Images of Cell-Based Assays (P Perner); Support Vector Machines and Applications (T B Trafalis & O O Oladunni); A Survey of Manifold-Based Learning Methods (X Huo et al.); and other papers. Readership: Graduate students in engineering, computer science, and business schools; researchers and practioners of data mining with emphazis of enterprise data mining."