Introduction To Neural Networks And Data Mining For Business Applications

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Introduction To Neural Networks And Data Mining For Business Applications

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Author : Kate A. Smith
language : en
Publisher:
Release Date : 1999

Introduction To Neural Networks And Data Mining For Business Applications written by Kate A. Smith and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Business categories.

Neural networks are a hot topic in the business community today. Also marketed as intelligent techniques, business intelligence and data mining, many businesses are now realising the potential of neural networks to give them a competitive edge. Nevertheless most neural network books are written by electrical engineers for electrical engineers, with a high level of mathematics. Those few books aimed at the business community invariably focus exclusively on financial prediction. Consequently, Introduction to Neural Networks and Data Mining for Business Applications is a ground breaking text. With a minimum of mathematics, it shows the potential of neural networks to unlock hidden information in data of various industries including retail, marketing, insurance, telecommunications, banking and finance, and operations management. The book covers the development of neural network research and its impact on business; the early neural Perceptron model and its limitations; backpropagation, the most commonly used learning paradigm in business applications; self-organisation; and adaptive resonance theory. Data mining is then covered including the purpose, methodology, and concepts of directed and undirected knowledge discovery. Other intelligent techniques often used in conjunction with neural networks are also covered, including genetic algorithms, fuzzy logic, and expert systems. The text concludes with a discussion of the future of neural networks research and applications. Extensive business case studies are used throughout the text to demonstrate techniques.

Neural Networks In Business

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Author : Kate A. Smith
language : en
Publisher: IGI Global
Release Date : 2003-01-01

Neural Networks In Business written by Kate A. Smith and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-01-01 with Computers categories.

"For professionals, students, and academics interested in applying neural networks to a variety of business applications, this reference book introduces the three most common neural network models and how they work. A wide range of business applications and a series of global case studies are presented to illustrate the neural network models provided. Each model or technique is discussed in detail and used to solve a business problem such as managing direct marketing, calculating foreign exchange rates, and improving cash flow forecasting."

Soft Computing For Knowledge Discovery And Data Mining

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Author : Oded Maimon
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-10-25

Soft Computing For Knowledge Discovery And Data Mining written by Oded Maimon 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 2007-10-25 with Computers categories.

Data mining is the science and technology of exploring large and complex bodies of data in order to discover useful patterns. It is extremely important because it enables modeling and knowledge extraction from abundant data availability. Soft Computing for Knowledge Discovery and Data Mining introduces soft computing methods extending the envelope of problems that data mining can solve efficiently. It presents practical soft-computing approaches in data mining. This edited volume by highly regarded authors, includes several contributors of the 2005, Data Mining and Knowledge Discovery Handbook. This book was written to provide investigators in the fields of information systems, engineering, computer science, statistics and management with a profound source for the role of soft computing in data mining. Not only does this book feature illustrations of various applications including manufacturing, medical, banking, insurance and others, but also includes various real-world case studies with detailed results. Soft Computing for Knowledge Discovery and Data Mining is designed for practitioners and researchers in industry. Practitioners and researchers may be particularly interested in the description of real world data mining projects performed with soft computing. This book is also suitable as a secondary textbook or reference for advanced-level students in information systems, engineering, computer science and statistics management.

Neural Networks In Business Techniques And Applications

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Author : Gupta, Jatinder N. D.
language : en
Publisher: IGI Global
Release Date : 2001-07-01

Neural Networks In Business Techniques And Applications written by Gupta, Jatinder N. D. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-07-01 with Computers categories.

Neural Networks in Business: Techniques and Applications aims to be an introductory reference book for professionals, students and academics interested in applying neural networks to a variety of business applications. The book introduces the three most common neural network models and how they work, followed by a wide range of business applications and a series of case studies presented from contributing authors around the world.

Data Mining For Business Analytics

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Author : Galit Shmueli
language : en
Publisher: John Wiley & Sons
Release Date : 2019-10-14

Data Mining For Business Analytics written by Galit Shmueli and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-14 with Mathematics categories.

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 Mining With Computational Intelligence

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Author : Lipo Wang
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-12-08

Data Mining With Computational Intelligence written by Lipo Wang 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 2005-12-08 with Computers categories.

Finding information hidden in data is as theoretically difficult as it is practically important. With the objective of discovering unknown patterns from data, the methodologies of data mining were derived from statistics, machine learning, and artificial intelligence, and are being used successfully in application areas such as bioinformatics, banking, retail, and many others. Wang and Fu present in detail the state of the art on how to utilize fuzzy neural networks, multilayer perceptron neural networks, radial basis function neural networks, genetic algorithms, and support vector machines in such applications. They focus on three main data mining tasks: data dimensionality reduction, classification, and rule extraction. The book is targeted at researchers in both academia and industry, while graduate students and developers of data mining systems will also profit from the detailed algorithmic descriptions.

Introduction To Data Mining And Its Applications

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Author : S. Sumathi
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-09-26

Introduction To Data Mining And Its Applications written by S. Sumathi 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 2006-09-26 with Computers categories.

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.

Soft Computing Applications In Business

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Author : Bhanu Prasad
language : en
Publisher: Springer
Release Date : 2008-04-10

Soft Computing Applications In Business written by Bhanu Prasad and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-04-10 with Technology & Engineering categories.

Soft computing techniques are widely used in most businesses. This book consists of several important papers on the applications of soft computing techniques for the business field. The soft computing techniques used in this book include (or very closely related to): Bayesian networks, biclustering methods, case-based reasoning, data mining, Dempster-Shafer theory, ensemble learning, evolutionary programming, fuzzy decision trees, hidden Markov models, intelligent agents, k-means clustering, maximum likelihood Hebbian learning, neural networks, opportunistic scheduling, probability distributions combined with Monte Carlo methods, rough sets, self organizing maps, support vector machines, uncertain reasoning, other statistical and machine learning techniques, and combinations of these techniques. The businesses or business problems addressed in this book include (or very closely related to): analysis of correlations between currency exchange rates, analysis of USA banks and Moody’s bank financial strength rating, arrears management, business risk identification, company audit fee evaluation, dental treatments, business internal control, intelligent tutoring systems and educational assessment, modeling agent behavior, motor insurance industry, personal loan defaults, pricing strategies for increasing the market share, pricing strategies in supply chain management, probabilistic sales forecasting, user relevance feedback analysis for online text retrieval, and world crude oil spot price forecasting.

Proceedings Of The Xiv International Symposium Symorg 2014

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Author : Aleksandar Marković
language : en
Publisher: FON
Release Date : 2014-06-05

Proceedings Of The Xiv International Symposium Symorg 2014 written by Aleksandar Marković and has been published by FON this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-05 with Business & Economics categories.

Data Mining And Business Intelligence

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Author : Dr. Jyotiranjan Hota
language : en
Publisher: BPB Publications
Release Date : 2025-05-20

Data Mining And Business Intelligence written by Dr. Jyotiranjan Hota and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-20 with Computers categories.

DESCRIPTION Data mining is crucial in business intelligence as it enables organizations to extract valuable insights and patterns from vast datasets, ultimately supporting informed decision-making, enhancing operational efficiency, and driving strategic growth. Validations, model building and interpretations are accomplished through databases, data warehouses, various supervised and unsupervised algorithms, tools for data modeling, descriptive analytics, diagnostic analytics, predictive analytics and prescriptive analytics to ensure accurate decision-making. This book systematically explores the core concepts and techniques of data mining and business intelligence. It begins by introducing fundamental principles and key methodologies, including regression, classification, association rule mining, and clustering. The text progresses to cover business intelligence architectures, data warehousing, and essential practices like data modeling, dashboard design, and data visualization using tools like Power BI. Furthermore, it delves into advanced topics such as text mining, big data analytics, and the ethical considerations surrounding data mining and business intelligence, ensuring a well-rounded understanding. Upon completing this book, readers will be competent in understanding various pre-processing techniques, applying appropriate data mining algorithms to large data sets, and conducting data analysis and interpretation to derive meaningful insights. They will also gain skills in data modeling and visualization to effectively communicate findings to business leaders and policymakers. Additionally, readers will develop an understanding of ethical considerations in data practices. WHAT YOU WILL LEARN ● Conducting pre-processing of data, applying appropriate algorithm to generate model summary and communicating the result effectively. ● Master data mining, BI principles, regression, classification, association rules, and clustering. ● Design BI architectures, ETL processes, data warehouses, and effective data visualizations. ● Utilize Power BI for data modeling, dashboard design, and create compelling data visualizations. ● Explore text mining, big data analytics, and the ethical dimensions of data practices. ● Implement regression, classification, association rule mining, and clustering techniques. ● Develop expertise in data mining, business intelligence, and ethical data application. WHO THIS BOOK IS FOR This textbook is written for a wide range of audiences, including professionals such as data analysts, business managers, IT specialists, analytics professionals, and researchers seeking to enhance their understanding of data-driven decision-making. It is also valuable for students who want to establish foundational knowledge in data mining and business intelligence. TABLE OF CONTENTS 1. Introduction to Data Mining and Business Intelligence 2. Regression and Classification Techniques with Applications 3. Concept and Application of Association Rule Mining Algorithm 4. Clustering 5. Introduction to Business Intelligence 6. Business Intelligence Architecture, Query and Reporting Practices 7. Advanced Data Mining and Business Intelligence Techniques 8. Data Mining and Business Intelligence Ethical Framework