The top ten algorithms in data mining / edited by Xindong Wu, Vipin Kumar.

Contributor(s):

Material type: Text

TextSeries: Chapman & Hall/CRC data mining and knowledge discovery seriesPublication details: Boca Raton : CRC Press, c2009.Description: xiii, 215 p. : ill. ; 25 cmISBN:

9781420089646
1420089641 (hard back : alk. paper)

Subject(s):

DDC classification:

005.74 22 To

LOC classification:

QA76.9.D343 T66 2009

Summary: The Top Ten Algorithms in Data Mining provides a description of each algorithm, discusses its impact, and reviews current and future research. Thoroughly evaluated by independent reviewers, each chapter focuses on a particular algorithm and is written by either the original authors of the algorithm or world-class researchers who have extensively studied the respective algorithm. The book concentrates on the following important algorithms: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. Examples illustrate how each algorithm works and highlight its overall performance in a real-world application. The text covers key topics―including classification, clustering, statistical learning, association analysis, and link mining―in data mining research and development as well as in data mining, machine learning, and artificial intelligence courses. By naming the leading algorithms in this field, this book encourages the use of data mining techniques in a broader realm of real-world applications. It should inspire more data mining researchers to further explore the impact and novel research issues of these algorithms.

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Holdings
Item type	Current library	Collection	Call number	Status	Notes	Date due	Barcode
Books	Bahrain ITC-DBS	Computer Studies	005.74 To (Browse shelf(Opens below))	Available	Dr. Md Asdaque		3000001618

"A Chapman & Hall book."

Includes bibliographical references and index.

The Top Ten Algorithms in Data Mining provides a description of each algorithm, discusses its impact, and reviews current and future research. Thoroughly evaluated by independent reviewers, each chapter focuses on a particular algorithm and is written by either the original authors of the algorithm or world-class researchers who have extensively studied the respective algorithm.

The book concentrates on the following important algorithms: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. Examples illustrate how each algorithm works and highlight its overall performance in a real-world application. The text covers key topics―including classification, clustering, statistical learning, association analysis, and link mining―in data mining research and development as well as in data mining, machine learning, and artificial intelligence courses.

By naming the leading algorithms in this field, this book encourages the use of data mining techniques in a broader realm of real-world applications. It should inspire more data mining researchers to further explore the impact and novel research issues of these algorithms.

The online book link:

https://www.amazon.com/Algorithms-Mining-Chapman-Knowledge-Discovery/dp/1420089641/ref=sr_1_1?dchild=1&keywords=9781420089646&qid=1613387904&s=books&sr=1-1

Dr. Md Asdaque

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