Processing ......
FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
 
Spectral Feature Selection for Data Mining
The one place to read news all over the world: LinkBasket. Mobile App too.
  • Title: Spectral Feature Selection for Data Mining
  • Author(s) Zheng Alan Zhao, Huan Liu
  • Publisher: Chapman and Hall/CRC (2011); eBook (Creative Commons Licensed, 2012)
  • License(s): CC BY 4.0
  • Hardcover/Paperback: 220 pages
  • eBook: PDF and Read Online
  • Language: English
  • ISBN-10: 1439862095
  • ISBN-13: 978-1439862094
  • Share This:  

Book Description

This book introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications. This technique represents a unified framework for supervised, unsupervised, and semisupervised feature selection.

About the Authors
  • Zheng Zhao is a research statistician at the SAS Institute, Inc.
  • Huan Liu is a professor of computer science and engineering at Arizona State University.
Reviews, Ratings, and Recommendations: Related Book Categories: Read and Download Links: Similar Books:
  • Mining of Massive Datasets (Jure Leskovec, et al)

    It focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically.

  • Data Mining for the Masses (Matthew North)

    This book uses simple examples, clear explanations and free, powerful, easy-to-use software to teach you the basics of data mining; techniques that can help you answer some of your toughest business questions.

  • Data Mining and Analysis: Fundamental Concepts and Algorithms

    This textbook provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification.

  • A Programmer's Guide to Data Mining (Ron Zacharski)

    This book is a tool for learning basic data mining techniques. If you are a programmer interested in learning a bit about data mining you might be interested in a beginner's hands-on guide as a first step. That's what this book provides.

  • An Introduction to Data Mining (Dr. Saed Sayad)

    This book presents fundamental concepts and algorithms for those learning data mining for the first time, provides both theoretical and practical coverage of all data mining topics. Includes extensive number of integrated examples and figures.

  • Data Mining Desktop Survival Guide (Graham William)

    This book thoroughly acquaints you with the new generation of data mining tools and techniques and shows you how to use them to make better business decisions. Assuming no prior knowledge of R or data mining/statistical techniques.

  • Mining Social Media: Finding Stories in Internet Data

    This book shows you how to use Python and key data analysis tools to find the stories buried in social media. Perform advanced data analysis using Python, Jupyter Notebooks, and the Pandas library.

  • Social Media Mining: An Introduction (Reza Zafarani, et al)

    This textbook introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining.

  • Mining the Web: Discovering Knowledge from Hypertext Data

    This is is the first book devoted entirely to techniques for producing knowledge from the vast body of unstructured Web data. Building on an initial survey of infrastructural issues-including Web crawling and indexing.

  • Twitter Data Analytics (Shamanth Kumar, et al)

    This book provides methods for harnessing Twitter data to discover solutions to complex inquiries. The brief introduces the process of collecting data through Twitter's APIs and offers strategies for curating large datasets.

  • O'Reilly® Mining the Social Web, 2nd Edition (Matthew A. Russell)

    This book shows you how to answer these questions like how can you tap into social data and discover who's connecting with whom, which insights are lurking just beneath the surface, and what people are talking about?

Book Categories
:
Other Categories
Resources and Links