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Download the fantastic book titled Feature Selection for Knowledge Discovery and Data Mining written by Huan Liu, available in its entirety in both PDF and EPUB formats for online reading. This page includes a concise summary, a preview of the book cover, and detailed information about "Feature Selection for Knowledge Discovery and Data Mining", which was released on 06 December 2012. We suggest perusing the summary before initiating your download. This book is a top selection for enthusiasts of the Computers genre.

Summary of Feature Selection for Knowledge Discovery and Data Mining by Huan Liu PDF

As computer power grows and data collection technologies advance, a plethora of data is generated in almost every field where computers are used. The com puter generated data should be analyzed by computers; without the aid of computing technologies, it is certain that huge amounts of data collected will not ever be examined, let alone be used to our advantages. Even with today's advanced computer technologies (e. g. , machine learning and data mining sys tems), discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Taking its simplest form, raw data are represented in feature-values. The size of a dataset can be measUJ·ed in two dimensions, number of features (N) and number of instances (P). Both Nand P can be enormously large. This enormity may cause serious problems to many data mining systems. Feature selection is one of the long existing methods that deal with these problems. Its objective is to select a minimal subset of features according to some reasonable criteria so that the original task can be achieved equally well, if not better. By choosing a minimal subset offeatures, irrelevant and redundant features are removed according to the criterion. When N is reduced, the data space shrinks and in a sense, the data set is now a better representative of the whole data population. If necessary, the reduction of N can also give rise to the reduction of P by eliminating duplicates.

Feature Selection for Knowledge Discovery and Data Mining

Detail About Feature Selection for Knowledge Discovery and Data Mining PDF

  • Author : Huan Liu
  • Publisher : Springer Science & Business Media
  • Genre : Computers
  • Total Pages : 225 pages
  • ISBN : 1461556899
  • PDF File Size : 30,7 Mb
  • Language : English
  • Rating : 4/5 from 21 reviews

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Feature Selection for Knowledge Discovery and Data Mining

Feature Selection for Knowledge Discovery and Data Mining
  • Publisher : Springer Science & Business Media
  • File Size : 49,8 Mb
  • Release Date : 06 December 2012
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As computer power grows and data collection technologies advance, a plethora of data is generated in almost every field where computers are used. The com puter generated data should be

Computational Methods of Feature Selection

Computational Methods of Feature Selection
  • Publisher : CRC Press
  • File Size : 47,5 Mb
  • Release Date : 29 October 2007
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Due to increasing demands for dimensionality reduction, research on feature selection has deeply and widely expanded into many fields, including computational statistics, pattern recognition, machine learning, data mining, and knowledge

Feature Selection for Knowledge Discovery and Data Mining

Feature Selection for Knowledge Discovery and Data Mining
  • Publisher : LAP Lambert Academic Publishing
  • File Size : 33,8 Mb
  • Release Date : 20 May 2024
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With advanced computer technologies and their omnipresent usage, data accumulates in a speed unmatchable by the human's capacity to process data. To meet this growing challenge, the research community of

Hierarchical Feature Selection for Knowledge Discovery

Hierarchical Feature Selection for Knowledge Discovery
  • Publisher : Springer
  • File Size : 26,7 Mb
  • Release Date : 29 November 2018
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This book is the first work that systematically describes the procedure of data mining and knowledge discovery on Bioinformatics databases by using the state-of-the-art hierarchical feature selection algorithms. The novelties

Data Mining and Knowledge Discovery Handbook

Data Mining and Knowledge Discovery Handbook
  • Publisher : Springer Science & Business Media
  • File Size : 26,7 Mb
  • Release Date : 28 May 2006
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Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and

Data Mining Methods for Knowledge Discovery

Data Mining Methods for Knowledge Discovery
  • Publisher : Springer Science & Business Media
  • File Size : 46,8 Mb
  • Release Date : 06 December 2012
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Data Mining Methods for Knowledge Discovery provides an introduction to the data mining methods that are frequently used in the process of knowledge discovery. This book first elaborates on the

Knowledge Discovery and Data Mining. Current Issues and New Applications

Knowledge Discovery and Data Mining. Current Issues and New Applications
  • Publisher : Springer Science & Business Media
  • File Size : 22,8 Mb
  • Release Date : 13 July 2007
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The Fourth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2000) was held at the Keihanna-Plaza, Kyoto, Japan, April 18 - 20, 2000. PAKDD 2000 provided an international forum for researchers and applica tion

Advances in Knowledge Discovery and Data Mining

Advances in Knowledge Discovery and Data Mining
  • Publisher : Springer
  • File Size : 31,9 Mb
  • Release Date : 16 June 2018
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This three-volume set, LNAI 10937, 10938, and 10939, constitutes the thoroughly refereed proceedings of the 22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018, held in Melbourne, VIC, Australia, in

Advances in Knowledge Discovery and Data Mining

Advances in Knowledge Discovery and Data Mining
  • Publisher : Springer
  • File Size : 26,5 Mb
  • Release Date : 06 April 2013
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The two-volume set LNAI 7818 + LNAI 7819 constitutes the refereed proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013, held in Gold Coast, Australia, in April 2013. The total

Spectral Feature Selection for Data Mining (Open Access)

Spectral Feature Selection for Data Mining (Open Access)
  • Publisher : CRC Press
  • File Size : 54,5 Mb
  • Release Date : 14 December 2011
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Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems