000			02610nam a2200313 i 4500
003			UkCbUP
005			20251211145246.0
008			210507s2022||||enk o ||1 0|eng|d
020			_a9781009099950 (ebook)
020			_z9781009098168 (hardback)
041			_aEnglish
082	0	0	_a514.7 _223/eng/20211029 _bDEY
100	1		_aDey, Tamal K.
100	1		_q(Tamal Krishna), _d1964- _eauthor.
245	1	0	_aComputational topology for data analysis / _cTamal Krishna Dey, Purdue University, Yusu Wang, University of California, San Diego.
260			_aCambridge, United Kingdom : _bCambridge University Press, _c2022.
300			_axix, 433 pages : _billustrations ;
500			_aTitle from publisher's bibliographic system (viewed on 18 Feb 2022).
505			_a1. Basics; 2. Complexes and homology groups; 3. Topological persistence; 4. General persistence; 5. Generators and optimality; 6. Topological analysis of point clouds; 7. Reeb graphs; 8. Topological analysis of graphs; 9. Cover, nerve and Mapper; 10. Discrete Morse theory and applications; 11. Multiparameter persistence and decomposition; 12. Multiparameter persistence and distances; 13. Topological persistence and machine learning.
520			_aTopological data analysis (TDA) has emerged recently as a viable tool for analyzing complex data, and the area has grown substantially both in its methodologies and applicability. Providing a computational and algorithmic foundation for techniques in TDA, this comprehensive, self-contained text introduces students and researchers in mathematics and computer science to the current state of the field. The book features a description of mathematical objects and constructs behind recent advances, the algorithms involved, computational considerations, as well as examples of topological structures or ideas that can be used in applications. It provides a thorough treatment of persistent homology together with various extensions - like zigzag persistence and multiparameter persistence - and their applications to different types of data, like point clouds, triangulations, or graph data. Other important topics covered include discrete Morse theory, the Mapper structure, optimal generating cycles, as well as recent advances in embedding TDA within machine learning frameworks.
650		0	_aTopology.
700	1		_aWang, Yusu,
700	1		_d1976- _eauthor.
776	0	8	_iPrint version: _z9781009098168.
856	4	0	_uhttps://ezproxy.lib.gla.ac.uk/login?url=https://doi.org/10.1017/9781009099950
856	4	0	_zConnect to e-book
907			_a.b40749034
942			_2ddc _cBOOKS
999			_c46354 _d46354