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006			m o d
007			cr|mn|---annan
008			202208s2022 x |||||o ||||eng|| d
020			_a9780367457808
020			_a9781003025245
020			_a9781032077703
040			_aoapen _coapen
041	0		_aeng
080			_a004
100	1		_aBacher, Johann _4auth
245	1	0	_aChapter 19 Unsupervised Methods _bClustering Methods
260			_bTaylor & Francis _c2022
300			_a1 electronic resource (19 p.)
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
506	0		_aOpen Access _2star _fUnrestricted online access
520			_aThe Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This second volume focuses on foundations and advances in data science, statistical modeling, and machine learning. It covers a range of key issues, including the management of big data in terms of record linkage, streaming, and missing data. Machine learning, agent-based and statistical modeling, as well as data quality in relation to digital trace and textual data, as well as probability, non-probability, and crowdsourced samples represent further foci. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors.
540			_aCreative Commons _fhttps://creativecommons.org/licenses/by-nc-nd/4.0/ _2cc
546			_aEnglish
650		0	_aИнформационные технологии _94550
653			_asurvey data, data analysis, data science, information technology, AI, socio-robotics, quantitative, survey methodology, ethics, ethical standards, privacy, replication, politics, survey design, social media, big data, social, human-robot interaction, machine learning, open data, data archives, data ownership, digital trace, unstructured data
700	1		_aPöge, Andreas _4auth
700	1		_aWenzig, Knut _4auth
773	1	0	_tHandbook of Computational Social Science, Volume 2 _7nnaa _oOAPEN Library UUID: 866251e4-af21-49fd-a795-9950f3c15530
856	4	0	_awww.oapen.org _uhttps://library.oapen.org/bitstream/id/ed20838e-612c-4371-9c89-3c3cd1710eac/9781003025245_10.4324_9781003025245-23.pdf _70 _zDownload
856	4	0	_awww.oapen.org _uhttps://library.oapen.org/handle/20.500.12657/57709 _70 _zDescription
909			_c255 _dRobiyakhon Olimjonova
942			_2udc _cEE
999			_c6203 _d6203