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Introduction to Data Mining and its Applications

Introduction to Data Mining and its Applications
Author: S. Sumathi
Publisher: Springer
Total Pages: 836
Release: 2006-10-12
Genre: Computers
ISBN: 3540343512

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This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in database systems, and presents 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, artificial intelligence, machine learning, neural networks, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing and data visualization.


Introduction to Data Mining and Its Applications

Introduction to Data Mining and Its Applications
Author: S. Sumathi
Publisher: Springer Science & Business Media
Total Pages: 836
Release: 2006-09-26
Genre: Computers
ISBN: 3540343504

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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.


Practical Applications of Data Mining

Practical Applications of Data Mining
Author: Sang Suh
Publisher: Jones & Bartlett Publishers
Total Pages: 436
Release: 2012
Genre: Computers
ISBN: 0763785873

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Introduction to data mining -- Association rules -- Classification learning -- Statistics for data mining -- Rough sets and bayes theories -- Neural networks -- Clustering -- Fuzzy information retrieval.


Introduction to Data Mining

Introduction to Data Mining
Author: Pang-Ning Tan
Publisher: Pearson Education India
Total Pages: 780
Release: 2016
Genre:
ISBN: 9332586055

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Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. Each major topic is organized into two chapters, beginni


Discovering Knowledge in Data

Discovering Knowledge in Data
Author: Daniel T. Larose
Publisher: John Wiley & Sons
Total Pages: 240
Release: 2005-01-28
Genre: Computers
ISBN: 0471687537

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Learn Data Mining by doing data mining Data mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets. Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include: * Data preprocessing and classification * Exploratory analysis * Decision trees * Neural and Kohonen networks * Hierarchical and k-means clustering * Association rules * Model evaluation techniques Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge. An Instructor's Manual presenting detailed solutions to all the problems in the book is available online.


Introduction to Data Mining and Analytics

Introduction to Data Mining and Analytics
Author: Kris Jamsa
Publisher: Jones & Bartlett Learning
Total Pages: 687
Release: 2020-02-03
Genre: Computers
ISBN: 1284210480

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Data Mining and Analytics provides a broad and interactive overview of a rapidly growing field. The exponentially increasing rate at which data is generated creates a corresponding need for professionals who can effectively handle its storage, analysis, and translation.


Introduction to Data Mining

Introduction to Data Mining
Author: Pang-Ning Tan
Publisher:
Total Pages: 864
Release: 2018-04-13
Genre: Data mining
ISBN: 9780273769224

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Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.


Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques
Author: Jiawei Han
Publisher: Elsevier
Total Pages: 740
Release: 2011-06-09
Genre: Computers
ISBN: 0123814804

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Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data


Data Mining Techniques and Applications

Data Mining Techniques and Applications
Author: Hongbo Du
Publisher:
Total Pages: 315
Release: 2010
Genre: Data mining
ISBN: 9781844808915

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This concise and approachable introduction to data mining selects a mixture of data mining techniques originating from statistics, machine learning and databases, and presents them in an algorithmic approach. Aimed primarily at undergraduate readers, it presents not only the fundamental principles and concepts of the subject in an easy-to-understand way, but also hands on, practical instruction on data mining techniques, that readers can put into practice as they go along using the freely downloadable Weka toolkit. Author Hongbo Du shares his years of commercial, as well as research-based, experience in the field through extensive examples and real-world case studies, highlighting how data mining solutions provided by software tools are used in practical problem solving. Covering not only traditional areas of data mining such as association, clustering and classification, this text also explains topics such as data warehousing, online-analytic processing, and text mining.