| 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 |
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.
정보제공 :
