Contemporary Perspectives in Data Mining

Contemporary Perspectives in Data Mining PDF

Author: Kenneth D. Lawrence

Publisher: IAP

Published: 2021-01-01

Total Pages: 159

ISBN-13: 164802145X

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The 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 business (banking, brokerage, and insurance), marketing (customer relationship, retailing, logistics, and travel), as well as in manufacturing, health care, fraud detection, homeland security and law enforcement.

Contemporary Perspectives in Data Mining, Volume 2

Contemporary Perspectives in Data Mining, Volume 2 PDF

Author: Kenneth D. Lawrence

Publisher: IAP

Published: 2015-07-01

Total Pages: 239

ISBN-13: 1681230895

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The 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.)

Contemporary Perspectives in Data Mining, Volume 1

Contemporary Perspectives in Data Mining, Volume 1 PDF

Author: Kenneth D. Lawrence

Publisher: IAP

Published: 2013-04-01

Total Pages: 255

ISBN-13: 1623960576

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The 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 form 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 seen in finance (banking, brokerage, insurance), marketing (customer relationships, retailing, logistics, travel), as well as in manufacturing, health care, fraud detection, home-land security, and law enforcement.

Contemporary Perspectives in Data Mining

Contemporary Perspectives in Data Mining PDF

Author: Kenneth D. Lawrence

Publisher: IAP

Published: 2017-09-01

Total Pages: 169

ISBN-13: 1641130563

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The 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 finance (banking, brokerage, and insurance), marketing (customer relationships, retailing, logistics, and travel), as well as in manufacturing, health care, fraud detection, homeland security, and law enforcement.

Analytics in Healthcare

Analytics in Healthcare PDF

Author: Christo El Morr

Publisher: Springer

Published: 2019-01-21

Total Pages: 105

ISBN-13: 3030045064

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This book offers a practical introduction to healthcare analytics that does not require a background in data science or statistics. It presents the basics of data, analytics and tools and includes multiple examples of their applications in the field. The book also identifies practical challenges that fuel the need for analytics in healthcare as well as the solutions to address these problems. In the healthcare field, professionals have access to vast amount of data in the form of staff records, electronic patient record, clinical findings, diagnosis, prescription drug, medical imaging procedure, mobile health, resources available, etc. Managing the data and analyzing it to properly understand it and use it to make well-informed decisions can be a challenge for managers and health care professionals. A new generation of applications, sometimes referred to as end-user analytics or self-serve analytics, are specifically designed for non-technical users such as managers and business professionals. The ability to use these increasingly accessible tools with the abundant data requires a basic understanding of the core concepts of data, analytics, and interpretation of outcomes. This book is a resource for such individuals to demystify and learn the basics of data management and analytics for healthcare, while also looking towards future directions in the field.

Machine Learning for Practical Decision Making

Machine Learning for Practical Decision Making PDF

Author: Christo El Morr

Publisher: Springer Nature

Published: 2022-11-29

Total Pages: 475

ISBN-13: 3031169905

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This book provides a hands-on introduction to Machine Learning (ML) from a multidisciplinary perspective that does not require a background in data science or computer science. It explains ML using simple language and a straightforward approach guided by real-world examples in areas such as health informatics, information technology, and business analytics. The book will help readers understand the various key algorithms, major software tools, and their applications. Moreover, through examples from the healthcare and business analytics fields, it demonstrates how and when ML can help them make better decisions in their disciplines. The book is chiefly intended for undergraduate and graduate students who are taking an introductory course in machine learning. It will also benefit data analysts and anyone interested in learning ML approaches.

Data Mining

Data Mining PDF

Author: Ian H. Witten

Publisher: Elsevier

Published: 2011-02-03

Total Pages: 665

ISBN-13: 0080890369

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Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Applications of Management Science

Applications of Management Science PDF

Author: Kenneth D. Lawrence

Publisher: Emerald Group Publishing

Published: 2018-08-06

Total Pages: 201

ISBN-13: 1787566528

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Volume 19 of Applications of Management Science focuses on the application of management science methodologies, data envelopment analysis and multi-criteria decision making.

Principles of Data Mining

Principles of Data Mining PDF

Author: David J. Hand

Publisher: MIT Press

Published: 2001-08-17

Total Pages: 594

ISBN-13: 9780262082907

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The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.

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