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001			978-3-319-57529-2
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008			170422s2017 sz | s |||| 0|eng d
020			_a9783319575292 _9978-3-319-57529-2
024	7		_a10.1007/978-3-319-57529-2 _2doi
050		4	_aQA76.9.D343
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072		7	_aUYQE _2bicssc
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072		7	_aUNF _2thema
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082	0	4	_a006.312 _223
245	1	0	_aAdvances in Knowledge Discovery and Data Mining _h[electronic resource] : _b21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23-26, 2017, Proceedings, Part II / _cedited by Jinho Kim, Kyuseok Shim, Longbing Cao, Jae-Gil Lee, Xuemin Lin, Yang-Sae Moon.
250			_a1st ed. 2017.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2017.
300			_aXXXII, 857 p. 252 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v10235
505	0		_aClassification and deep learning -- Social network and graph mining -- Privacy-preserving mining and security/risk applications -- Spatio-temporal and sequential data mining -- Clustering and anomaly detection -- Recommender system -- Feature selection -- Text and opinion mining -- Clustering and matrix factorization -- Dynamic, stream data mining -- Novel models and algorithms -- Behavioral data mining -- Graph clustering and community detection -- Dimensionality reduction.
520			_aThis two-volume set, LNAI 10234 and 10235, constitutes the thoroughly refereed proceedings of the 21st Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2017, held in Jeju, South Korea, in May 2017. The 129 full papers were carefully reviewed and selected from 458 submissions. They are organized in topical sections named: classification and deep learning; social network and graph mining; privacy-preserving mining and security/risk applications; spatio-temporal and sequential data mining; clustering and anomaly detection; recommender system; feature selection; text and opinion mining; clustering and matrix factorization; dynamic, stream data mining; novel models and algorithms; behavioral data mining; graph clustering and community detection; dimensionality reduction.
650		0	_aData mining.
650		0	_aArtificial intelligence.
650		0	_aInformation storage and retrieval systems.
650		0	_aApplication software.
650		0	_aDatabase management.
650		0	_aData protection.
650	1	4	_aData Mining and Knowledge Discovery.
650	2	4	_aArtificial Intelligence.
650	2	4	_aInformation Storage and Retrieval.
650	2	4	_aComputer and Information Systems Applications.
650	2	4	_aDatabase Management.
650	2	4	_aData and Information Security.
700	1		_aKim, Jinho. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aShim, Kyuseok. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aCao, Longbing. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aLee, Jae-Gil. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aLin, Xuemin. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aMoon, Yang-Sae. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783319575285
776	0	8	_iPrinted edition: _z9783319575308
830		0	_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v10235
856	4	0	_uhttps://doi.org/10.1007/978-3-319-57529-2
912			_aZDB-2-SCS
912			_aZDB-2-SXCS
912			_aZDB-2-LNC
942			_cSPRINGER
999			_c173255 _d173255