Data mining and machine learning: (Record no. 4122)

MARC details
000 -LEADER
fixed length control field 02538nam a22002177a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20221201122403.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
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020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781108473989
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.312
Item number ZAK
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Zaki, Mohammed J.
245 ## - TITLE STATEMENT
Title Data mining and machine learning:
Remainder of title fundamental concepts and algorithms
250 ## - EDITION STATEMENT
Edition statement 2nd
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc. Cambridge University Press
Place of publication, distribution, etc. Cambridge
Date of publication, distribution, etc. 2020
300 ## - PHYSICAL DESCRIPTION
Extent xii, 766 p.
365 ## - TRADE PRICE
Price type code GBP
Price amount 57.99
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Table of Contents<br/>1. Data mining and analysis<br/>Part I. Data Analysis Foundations:<br/>2. Numeric attributes<br/>3. Categorical attributes<br/>4. Graph data<br/>5. Kernel methods<br/>6. High-dimensional data<br/>7. Dimensionality reduction<br/>Part II. Frequent Pattern Mining:<br/>8. Itemset mining<br/>9. Summarizing itemsets<br/>10. Sequence mining<br/>11. Graph pattern mining<br/>12. Pattern and rule assessment<br/>Part III. Clustering:<br/>13. Representative-based clustering<br/>14. Hierarchical clustering<br/>15. Density-based clustering<br/>16. Spectral and graph clustering<br/>17. Clustering validation<br/>Part IV. Classification:<br/>18. Probabilistic classification<br/>19. Decision tree classifier<br/>20. Linear discriminant analysis<br/>21. Support vector machines<br/>22. Classification assessment<br/>Part V. Regression:<br/>23. Linear regression<br/>24. Logistic regression<br/>25. Neural networks<br/>26. Deep learning<br/>27. Regression evaluation.
520 ## - SUMMARY, ETC.
Summary, etc. The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.<br/><br/>Covers both core methods and cutting-edge research, including deep learning<br/>Offers an algorithmic approach with open-source implementations<br/>Short, self-contained chapters with class-tested examples and exercises allow flexibility in course design and ready reference
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data mining
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Meira, Wagner
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Book
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Bill No Bill Date Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Full call number Accession Number Date last seen Copy number Cost, replacement price Price effective from Koha item type
    Dewey Decimal Classification     IT & Decisions Sciences IN180 27-10-2022 Indian Institute of Management LRC Indian Institute of Management LRC General Stacks 12/01/2022 Bharatiya Sahitya Bhavana 3736.59   006.312 ZAK 003855 12/01/2022 1 3736.59 12/01/2022 Book

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