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Data mining algorithms........... Rajan Chattamvelli.

By: Material type: TextTextPublication details: New Delhi : Narosa Publishing House, 2011.Description: 1.v ( unpaged ) (R13,SH 02)ISBN:
  • 8184871201 (paperback)
  • 9788184871203 (paperback)
DDC classification:
  • 006.312
Online resources: Summary: Researchers and Professionals in data mining and related fields should be familiar with different models and standard algorithms in use to have a clear understanding of the concepts involved. Data Mining Algorithms provides the reader with unprecedented insights into the working of various algorithms. Several novel algorithms in association rules, decision trees, statistics, information retrieval etc are clearly defined, and thoroughly discussed. The well-known page rank metric used by search engines is extended in multiple ways in chapter 5 to improve the quality of search results. A highly informative discussion of support vector machines appears in chapter 6. Students in data mining, machine learning, soft computing and statistics will find a wealth of useful and reliable information in this unique and indispensable volume. Scientists, engineers, senior undergraduate and graduate students in applied sciences will all find this book to be extremely useful to sharpen their skills, to improve their general knowledge, and to explore the computational aspects of complex models and algorithms presented.
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Book Book Air University Multan Campus Library NFIC 006.312 (Browse shelf(Opens below)) Available P001211

Researchers and Professionals in data mining and related fields should be familiar with different models and standard algorithms in use to have a clear understanding of the concepts involved. Data Mining Algorithms provides the reader with unprecedented insights into the working of various algorithms. Several novel algorithms in association rules, decision trees, statistics, information retrieval etc are clearly defined, and thoroughly discussed. The well-known page rank metric used by search engines is extended in multiple ways in chapter 5 to improve the quality of search results. A highly informative discussion of support vector machines appears in chapter 6. Students in data mining, machine learning, soft computing and statistics will find a wealth of useful and reliable information in this unique and indispensable volume. Scientists, engineers, senior undergraduate and graduate students in applied sciences will all find this book to be extremely useful to sharpen their skills, to improve their general knowledge, and to explore the computational aspects of complex models and algorithms presented.

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