000			04290nam a22006015i 4500
001			978-3-030-02985-2
003			DE-He213
005			20240423125041.0
007			cr nn 008mamaa
008			190430s2019 sz | s |||| 0|eng d
020			_a9783030029852 _9978-3-030-02985-2
024	7		_a10.1007/978-3-030-02985-2 _2doi
050		4	_aQA76.9.D343
072		7	_aUNF _2bicssc
072		7	_aUYQE _2bicssc
072		7	_aCOM021030 _2bisacsh
072		7	_aUNF _2thema
072		7	_aUYQE _2thema
082	0	4	_a006.312 _223
100	1		_aMeng, Lei. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aAdaptive Resonance Theory in Social Media Data Clustering _h[electronic resource] : _bRoles, Methodologies, and Applications / _cby Lei Meng, Ah-Hwee Tan, Donald C. Wunsch II.
250			_a1st ed. 2019.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2019.
300			_aXV, 190 p. 53 illus., 34 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aAdvanced Information and Knowledge Processing, _x2197-8441
505	0		_aPart 1: Theories -- Introduction -- Clustering and Extensions in the Social Media Domain -- Adaptive Resonance Theory (ART) for Social Media Analytics -- Part II: Applications -- Personalized Web Image Organization -- Socially-Enriched Multimedia Data Co-Clustering -- Community Discovery in Heterogeneous Social Networks -- Online Multimodal Co-Indexing and Retrieval of Social Media Data -- Concluding Remarks.
520			_aSocial media data contains our communication and online sharing, mirroring our daily life. This book looks at how we can use and what we can discover from such big data: Basic knowledge (data & challenges) on social media analytics Clustering as a fundamental technique for unsupervised knowledge discovery and data mining A class of neural inspired algorithms, based on adaptive resonance theory (ART), tackling challenges in big social media data clustering Step-by-step practices of developing unsupervised machine learning algorithms for real-world applications in social media domain Adaptive Resonance Theory in Social Media Data Clustering stands on the fundamental breakthrough in cognitive and neural theory, i.e. adaptive resonance theory, which simulates how a brain processes information to perform memory, learning, recognition, and prediction. It presents initiatives on the mathematical demonstration of ART’s learning mechanisms in clustering, and illustrates how to extend the base ART model to handle the complexity and characteristics of social media data and perform associative analytical tasks. Both cutting-edge research and real-world practices on machine learning and social media analytics are included in the book and if you wish to learn the answers to the following questions, this book is for you: How to process big streams of multimedia data? How to analyze social networks with heterogeneous data? How to understand a user’s interests by learning from online posts and behaviors? How to create a personalized search engine by automatically indexing and searching multimodal information resources?
650		0	_aData mining.
650		0	_aAlgorithms.
650		0	_aCognitive psychology.
650		0	_aPattern recognition systems.
650	1	4	_aData Mining and Knowledge Discovery.
650	2	4	_aAlgorithms.
650	2	4	_aCognitive Psychology.
650	2	4	_aAutomated Pattern Recognition.
700	1		_aTan, Ah-Hwee. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aWunsch II, Donald C. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783030029845
776	0	8	_iPrinted edition: _z9783030029869
830		0	_aAdvanced Information and Knowledge Processing, _x2197-8441
856	4	0	_uhttps://doi.org/10.1007/978-3-030-02985-2
912			_aZDB-2-SCS
912			_aZDB-2-SXCS
942			_cSPRINGER
999			_c173664 _d173664