Modern Algorithms of Cluster Analysis (Record no. 75396)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03527nam a22005655i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-319-69308-8 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220801213621.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 171229s2018 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783319693088 |
| -- | 978-3-319-69308-8 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.3 |
| 100 1# - AUTHOR NAME | |
| Author | Wierzchoń, Slawomir. |
| 245 10 - TITLE STATEMENT | |
| Title | Modern Algorithms of Cluster Analysis |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2018. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XX, 421 p. 51 illus. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Studies in Big Data, |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | This book provides the reader with a basic understanding of the formal concepts of the cluster, clustering, partition, cluster analysis etc. The book explains feature-based, graph-based and spectral clustering methods and discusses their formal similarities and differences. Understanding the related formal concepts is particularly vital in the epoch of Big Data; due to the volume and characteristics of the data, it is no longer feasible to predominantly rely on merely viewing the data when facing a clustering problem. Usually clustering involves choosing similar objects and grouping them together. To facilitate the choice of similarity measures for complex and big data, various measures of object similarity, based on quantitative (like numerical measurement results) and qualitative features (like text), as well as combinations of the two, are described, as well as graph-based similarity measures for (hyper) linked objects and measures for multilayered graphs. Numerous variants demonstrating how such similarity measures can be exploited when defining clustering cost functions are also presented. In addition, the book provides an overview of approaches to handling large collections of objects in a reasonable time. In particular, it addresses grid-based methods, sampling methods, parallelization via Map-Reduce, usage of tree-structures, random projections and various heuristic approaches, especially those used for community detection. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Kłopotek, Mieczyslaw. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-319-69308-8 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2018. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Big data. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Mathematics. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Quantitative research. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Big Data. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Applications of Mathematics. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data Analysis and Big Data. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2197-6511 ; |
| 912 ## - | |
| -- | ZDB-2-ENG |
| 912 ## - | |
| -- | ZDB-2-SXE |
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