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Clustering methods for big data analytics : techniques, toolboxes and applications

Clustering methods for big data analytics : techniques, toolboxes and applications

자료유형
단행본
개인저자
Nasraoui, Olfa. Ben N'Cir, Chiheb-Eddine.
서명 / 저자사항
Clustering methods for big data analytics : techniques, toolboxes and applications / Olfa Nasraoui, Chiheb-Eddine Ben N'Cir, editors.
발행사항
Cham :   Springer,   c2019.  
형태사항
ix, 187 p. : ill. (some col.) ; 25 cm.
총서사항
Unsupervised and semi-supervised learning
ISBN
9783319978635 (hbk.) 9783319978642 (ebk.)
서지주기
Includes bibliographical references and index.
일반주제명
Big data. Cluster analysis. Data mining. Artificial intelligence. Business mathematics & systems. Pattern recognition. Communications engineering --telecommunications.
000 00000nam u2200205 a 4500
001 000045972701
005 20190304095343
008 190228s2019 sz a b 001 0 eng d
020 ▼a 9783319978635 (hbk.)
020 ▼a 9783319978642 (ebk.)
040 ▼a 211009 ▼c 211009 ▼d 211009
082 0 4 ▼a 006.312 ▼2 23
084 ▼a 006.312 ▼2 DDCK
090 ▼a 006.312 ▼b C649
245 0 0 ▼a Clustering methods for big data analytics : ▼b techniques, toolboxes and applications / ▼c Olfa Nasraoui, Chiheb-Eddine Ben N'Cir, editors.
260 ▼a Cham : ▼b Springer, ▼c c2019.
300 ▼a ix, 187 p. : ▼b ill. (some col.) ; ▼c 25 cm.
490 1 ▼a Unsupervised and semi-supervised learning
504 ▼a Includes bibliographical references and index.
650 0 ▼a Big data.
650 0 ▼a Cluster analysis.
650 0 ▼a Data mining.
650 0 ▼a Artificial intelligence.
650 0 ▼a Business mathematics & systems.
650 0 ▼a Pattern recognition.
650 0 ▼a Communications engineering ▼x telecommunications.
700 1 ▼a Nasraoui, Olfa.
700 1 ▼a Ben N'Cir, Chiheb-Eddine.
830 0 ▼a Unsupervised and semi-supervised learning.
945 ▼a KLPA

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/서고6층/ 청구기호 006.312 C649 등록번호 111805559 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

목차

Introduction.- Clustering large scale data.- Clustering heterogeneous data.- Distributed clustering methods.- Clustering structured and unstructured data.- Clustering and unsupervised learning for deep learning.- Deep learning methods for clustering.- Clustering high speed cloud, grid, and streaming data.- Extension of partitioning, model based, density based, grid based, fuzzy and evolutionary clustering methods for big data analysis.- Large documents and textual data clustering.- Applications of big data clustering methods.- Clustering multimedia and multi-structured data.- Large-scale recommendation systems and social media systems.- Clustering multimedia and multi-structured data.- Real life applications of big data clustering.- Validation measures for big data clustering methods.- Conclusion.


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