Data Mining

Concepts and Techniques

Author: Jiawei Han

Publisher: Morgan Kaufmann

ISBN: 9781558604896

Category: Computers

Page: 550

View: 6879

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Data warehouse and OLAP technology for data mining. Data preprocessing. Data mining primitives, languages, and system architecture. Concept description: characterization and comparison. Mining association rules in large databases. Classification and prediction. Cluster analysis. Mining complex types of data. Applications and trends in data mining. Appendix.
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Data Mining

Practical Machine Learning Tools and Techniques, Second Edition

Author: Ian H. Witten,Eibe Frank

Publisher: Elsevier

ISBN: 9780080477022

Category: Computers

Page: 560

View: 4252

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Data Mining, Second Edition, describes data mining techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights of this new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; and much more. This text is designed for information systems practitioners, programmers, consultants, developers, information technology managers, specification writers as well as professors and students of graduate-level data mining and machine learning courses. Algorithmic methods at the heart of successful data mining—including tried and true techniques as well as leading edge methods Performance improvement techniques that work by transforming the input or output
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Data Mining

Concepts, Models, Methods, and Algorithms

Author: Mehmed Kantardzic

Publisher: John Wiley & Sons

ISBN: 1118029135

Category: Computers

Page: 520

View: 6100

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This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces, to extract new information for decision making. The goal of this book is to provide a single introductory source, organized in a systematic way, in which we could direct the readers in analysis of large data sets, through the explanation of basic concepts, models and methodologies developed in recent decades. If you are an instructor or professor and would like to obtain instructor’s materials, please visit http://booksupport.wiley.com If you are an instructor or professor and would like to obtain a solutions manual, please send an email to: [email protected]
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Data Mining, Southeast Asia Edition

Author: Jiawei Han,Jian Pei,Micheline Kamber

Publisher: Elsevier

ISBN: 9780080475585

Category: Computers

Page: 800

View: 3986

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Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data— including stream data, sequence data, graph structured data, social network data, and multi-relational data. A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects Complete classroom support for instructors at www.mkp.com/datamining2e companion site
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Data Mining

A Knowledge Discovery Approach

Author: Krzysztof J. Cios,Witold Pedrycz,Roman W. Swiniarski,Lukasz Andrzej Kurgan

Publisher: Springer Science & Business Media

ISBN: 0387367950

Category: Computers

Page: 606

View: 1823

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This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribes the sequence in which data mining projects should be performed, from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes Data Mining from other texts in this area. The book provides a suite of exercises and includes links to instructional presentations. Furthermore, it contains appendices of relevant mathematical material.
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Data Mining: A Heuristic Approach

A Heuristic Approach

Author: Abbass, Hussein A.

Publisher: IGI Global

ISBN: 9781591400110

Category: Computers

Page: 310

View: 8886

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Real life problems are known to be messy, dynamic and multi-objective, and involve high levels of uncertainty and constraints. Because traditional problem-solving methods are no longer capable of handling this level of complexity, heuristic search methods have attracted increasing attention in recent years for solving such problems. Inspired by nature, biology, statistical mechanics, physics and neuroscience, heuristics techniques are used to solve many problems where traditional methods have failed. Data Mining: A Heuristic Approach will be a repository for the applications of these techniques in the area of data mining.
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Principles of Data Mining

Author: David J. Hand,Heikki Mannila,Professor in the Department of Statistics David J Hand,Padhraic Smyth

Publisher: MIT Press

ISBN: 9780262082907

Category: Computers

Page: 546

View: 1686

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Measuremente and Data. Visualizing and Exploring Data. Data Analysis and Uncertainty. A Systematic Overview of Data Mining Algorithms. Models and Patterns. Score Functions for Data Mining Algorithms. Serach and Optimization Methods. Descriptive Modeling. Predictive Modeling for Classification. Predictive Modeling for Regression. Data Organization and Databases. Finding Patterns and Rules. Retrieval by Content.
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Data Mining

Building Competitive Advantage

Author: Robert Groth

Publisher: Prentice Hall

ISBN: 9780130862716

Category: Computers

Page: 266

View: 536

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8627A-4 "Finally, here's a book that explains in plain English what data mining is and how it's used to improve a company's bottom line . . . Groth takes a very complex and vast field and makes it comprehensible." Miguel A. Castro, Ph.D., President, Dovetail Solutions Data mining business solutions-practical, up-to-date, and hands-on! With data mining, you can achieve competitive advantage from the data you've already paid to compile. Data Mining: Building Competitive Advantage shows you how. You won't just learn the theory and concepts of data mining; you'll discover how to apply them-hands-on, through real applications! Coverage includes: Case studies in banking, finance, retail, healthcare, direct marketing, and telecommunications The data mining process, start to finish Today's newest, most successful approaches and algorithms Data mining pitfalls-and how to avoid them A close look at industry-leading tools from Angoss(TM) and RightPoint(TM) Whether you're a manager, marketer, consultant, analyst, or database professional, Robert Groth will help you master data mining-and deliver all the competitive advantage it promises. About the Website The accompanying website includes full trial editions of two of the world's leading desktop data mining tools, Angoss KnowledgeSEEKER(R) and RightPoint DataCruncher.
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Data Mining for Business Applications

Author: Longbing Cao,Philip S. Yu,Chengqi Zhang,Huaifeng Zhang

Publisher: Springer Science & Business Media

ISBN: 0387794204

Category: Computers

Page: 302

View: 3903

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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.
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Advanced Data Mining Techniques

Author: David L. Olson,Dursun Delen

Publisher: Springer Science & Business Media

ISBN: 9783540769170

Category: Business & Economics

Page: 180

View: 6234

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This book covers the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. The book is organized in three parts. Part I introduces concepts. Part II describes and demonstrates basic data mining algorithms. It also contains chapters on a number of different techniques often used in data mining. Part III focuses on business applications of data mining.
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