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Modern technologies for big data classification and clustering

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Modern technologies for big data classification and clustering/ Hari Seetha, M. Narasimha Murty, and B.K. Tripathy, editors.
其他作者:	Seetha, Hari,
出版者:	Hershey, Pennsylvania :IGI Global, : [2018],
面頁冊數:	1 online resource (xxi, 360 p.)
標題:	Big data. -
電子資源:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-2805-0
ISBN:	9781522528067 (ebook)

Modern technologies for big data classification and clustering
Modern technologies for big data classification and clustering[electronic resource] /Hari Seetha, M. Narasimha Murty, and B.K. Tripathy, editors. - Hershey, Pennsylvania :IGI Global,[2018] - 1 online resource (xxi, 360 p.)

Includes bibliographical references and index.

Chapter 1. Uncertainty-based clustering algorithms for large data sets -- Chapter 2. Sentiment mining approaches for big data classification and clustering -- Chapter 3. Data compaction techniques -- Chapter 4. Methodologies and technologies to retrieve information from text sources -- Chapter 5. Twitter data analysis -- Chapter 6. Use of social network analysis in telecommunication domain -- Chapter 7. A review on spatial big data analytics and visualization -- Chapter 8. A survey on overlapping communities in large-scale social networks -- Chapter 9. A brief study of approaches to text feature selection -- Chapter 10. Biological big data analysis and visualization: a survey.

Restricted to subscribers or individual electronic text purchasers.

"This book provides an analysis of large data in the field of classification and clustering by presenting algorithms and comparative analysis in the form of their effectiveness and efficiency. It covers topics such as handling large data with conventional data mining, machine learning algorithms and information about new technologies, algorithms and platforms developed for handling large data"--

ISBN: 9781522528067 (ebook)Subjects--Topical Terms:

981821
Big data.

LC Class. No.: QA76.9.B45 / M63 2018e

Dewey Class. No.: 005.7

Modern technologies for big data classification and clustering
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245 0 0 $a Modern technologies for big data classification and clustering $h [electronic resource] / $c Hari Seetha, M. Narasimha Murty, and B.K. Tripathy, editors.
260 $a Hershey, Pennsylvania : $b IGI Global, $c [2018]
300 $a 1 online resource (xxi, 360 p.)
504 $a Includes bibliographical references and index.
505 0 $a Chapter 1. Uncertainty-based clustering algorithms for large data sets -- Chapter 2. Sentiment mining approaches for big data classification and clustering -- Chapter 3. Data compaction techniques -- Chapter 4. Methodologies and technologies to retrieve information from text sources -- Chapter 5. Twitter data analysis -- Chapter 6. Use of social network analysis in telecommunication domain -- Chapter 7. A review on spatial big data analytics and visualization -- Chapter 8. A survey on overlapping communities in large-scale social networks -- Chapter 9. A brief study of approaches to text feature selection -- Chapter 10. Biological big data analysis and visualization: a survey.
506 $a Restricted to subscribers or individual electronic text purchasers.
520 3 $a "This book provides an analysis of large data in the field of classification and clustering by presenting algorithms and comparative analysis in the form of their effectiveness and efficiency. It covers topics such as handling large data with conventional data mining, machine learning algorithms and information about new technologies, algorithms and platforms developed for handling large data"-- $c Provided by publisher.
650 0 $a Big data. $3 981821
650 0 $a Data mining. $3 528622
650 0 $a Cluster analysis. $3 562949
650 0 $a Classification $x Nonbook materials. $3 1155872
650 0 $a Document clustering. $3 599622
700 1 $a Seetha, Hari, $d 1970- $e editor. $3 1155870
700 1 $a Murty, M. Narasimha, $e editor. $3 1155871
700 1 $a Tripathy, B. K., $d 1957- $e editor. $3 1002275
856 4 0 $u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-2805-0