Data Mining

Data Mining
Publisher Morgan Kaufmann
Release Date
Category Computers
Total Pages 654
ISBN 9780128043578
Rating 4/5 from 21 reviews
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Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. Please visit the book companion website at http://www.cs.waikato.ac.nz/ml/weka/book.html It contains Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc. Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface Includes open-access online courses that introduce practical applications of the material in the book

Data Mining
  • Author : Ian H. Witten,Eibe Frank,Mark A. Hall,Christopher J. Pal
  • Publisher : Morgan Kaufmann
  • Release Date : 2016-10-01

Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches

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Data Mining
  • Author : Ian H. Witten,Eibe Frank
  • Publisher : Morgan Kaufmann
  • Release Date : 2000

This book offers a thorough grounding in machine learning concepts combined with practical advice on applying machine learning tools and techniques in real-world data mining situations. Clearly written and effectively illustrated, this book is ideal for anyone involved at any level in the work of extracting usable knowledge from large

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Data Mining  Concepts and Techniques
  • Author : Jiawei Han,Jian Pei,Micheline Kamber
  • Publisher : Elsevier
  • Release Date : 2011-06-09

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).

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IPython Interactive Computing and Visualization Cookbook
  • Author : Cyrille Rossant
  • Publisher : Packt Publishing Ltd
  • Release Date : 2014-09-25

Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists... Basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods.

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Practical Machine Learning for Data Analysis Using Python
  • Author : Abdulhamit Subasi
  • Publisher : Academic Press
  • Release Date : 2020-06-05

Practical Machine Learning for Data Analysis Using Python is a problem solver’s guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It

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Encyclopedia of Machine Learning
  • Author : Claude Sammut,Geoffrey I. Webb
  • Publisher : Springer Science & Business Media
  • Release Date : 2011-03-28

This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.

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Data Mining and Data Warehousing
  • Author : Parteek Bhatia
  • Publisher : Cambridge University Press
  • Release Date : 2019-04-30

Written in lucid language, this valuable textbook brings together fundamental concepts of data mining and data warehousing in a single volume. Important topics including information theory, decision tree, Nave Bayes classifier, distance metrics, partitioning clustering, associate mining, data marts and operational data store are discussed comprehensively. The textbook is written

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Advances in Knowledge Discovery and Data Mining
  • Author : PACIFIC-ASIA CONFERENCE ON KNOWLEDGE DIS,Honghua Dai,Ramakrishnan Srikant,Chengqi Zhang
  • Publisher : Springer Science & Business Media
  • Release Date : 2004-05-11

This book constitutes the refereed proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data mining, PAKDD 2004, held in Sydney, Australia in May 2004. The 50 revised full papers and 31 revised short papers presented were carefully reviewed and selected from a total of 238 submissions. The papers are organized in topical sections

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Introducing Data Science
  • Author : Davy Cielen,Arno Meysman
  • Publisher : Simon and Schuster
  • Release Date : 2016-05-02

Summary Introducing Data Science teaches you how to accomplish the fundamental tasks that occupy data scientists. Using the Python language and common Python libraries, you'll experience firsthand the challenges of dealing with data at scale and gain a solid foundation in data science. Purchase of the print book includes a

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Machine Learning for Data Streams
  • Author : Albert Bifet,Ricard Gavalda,Geoff Holmes,Bernhard Pfahringer
  • Publisher : MIT Press
  • Release Date : 2018-03-16

A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework. Today many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are so-called data streams, arriving sequentially and at high speed. Analysis

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Data Mining
  • Author : Ian H. Witten,Eibe Frank
  • Publisher : Morgan Kaufmann Publishers
  • Release Date : 2005

Much anticipated second edition of the highly-acclaimed reference on data mining and machine learning.

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Practical Machine Learning
  • Author : Sunila Gollapudi
  • Publisher : Packt Publishing Ltd
  • Release Date : 2016-01-30

Tackle the real-world complexities of modern machine learning with innovative, cutting-edge, techniques About This Book Fully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and Spark Comprehensive practical solutions taking you into the future of machine learning Go a step further and

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Statistical and Machine Learning Data Mining
  • Author : Bruce Ratner
  • Publisher : CRC Press
  • Release Date : 2012-02-28

The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and Analysis for Database Marketing: Effective

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Advances in Data Mining  Applications and Theoretical Aspects
  • Author : Petra Perner
  • Publisher : Springer
  • Release Date : 2013-07-11

This book constitutes the refereed proceedings of the 13th Industrial Conference on Data Mining, ICDM 2013, held in New York, NY, in July 2013. The 22 revised full papers presented were carefully reviewed and selected from 112 submissions. The topics range from theoretical aspects of data mining to applications of data mining, such as

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Data Mining
  • Author : Witten, Ian H. Witten
  • Publisher : Unknown
  • Release Date : 2011

Read online Data Mining written by Witten, Ian H. Witten, published by which was released on 2011. Download full Data Mining Books now! Available in PDF, ePub and Kindle.

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