000 | 06362nam a2200769 i 4500 | ||
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001 | 7084069 | ||
003 | IEEE | ||
005 | 20200413152917.0 | ||
006 | m eo d | ||
007 | cr cn |||m|||a | ||
008 | 150426s2015 caua foab 000 0 eng d | ||
020 |
_a9781627056618 _qebook |
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020 |
_z9781627056601 _qprint |
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024 | 7 |
_a10.2200/S00625ED1V01Y201502DMK010 _2doi |
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035 | _a(CaBNVSL)swl00404858 | ||
035 | _a(OCoLC)908031780 | ||
040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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050 | 4 |
_aQA76.9.D343 _bW255 2015 |
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082 | 0 | 4 |
_a006.312 _223 |
100 | 1 |
_aWang, Chi., _eauthor. |
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245 | 1 | 0 |
_aMining latent entity structures / _cChi Wang, Jiawei Han. |
264 | 1 |
_aSan Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : _bMorgan & Claypool, _c2015. |
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300 |
_a1 PDF (xi, 147 pages) : _billustrations. |
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336 |
_atext _2rdacontent |
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337 |
_aelectronic _2isbdmedia |
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338 |
_aonline resource _2rdacarrier |
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490 | 1 |
_aSynthesis lectures on data mining and knowledge discovery, _x2151-0075 ; _v# 10 |
|
538 | _aMode of access: World Wide Web. | ||
538 | _aSystem requirements: Adobe Acrobat Reader. | ||
500 | _aPart of: Synthesis digital library of engineering and computer science. | ||
504 | _aIncludes bibliographical references (pages 141-145). | ||
505 | 0 | _a1. Introduction -- 1.1 Motivation -- 1.2 Data model: a text-rich heterogeneous information network modeL -- 1.3 Latent entity structure -- 1.4 The mining framework -- 1.4.1 Hierarchical topic and community discovery -- 1.4.2 Topical phrase mining -- 1.4.3 Entity topical role analysis -- 1.4.4 Entity relationship mining -- | |
505 | 8 | _a2. Hierarchical topic and community discovery -- 2.1 Generative model for text or homogeneous networks -- 2.2 Generative model for heterogeneous network -- 2.2.1 The basic model -- 2.2.2 Learning link-type weights -- 2.2.3 Shape of hierarchy -- 2.3 Empirical analysis -- 2.3.1 Efficacy of subtopic discovery -- 2.3.2 Topical hierarchy quality -- 2.3.3 Case study -- | |
505 | 8 | _a3. Topical phrase mining -- 3.1 Criteria of good phrases and topical phrases -- 3.2 KERT: mining phrases in short, content-representative text -- 3.2.1 Phrase quality -- 3.2.2 Topical phrase quality -- 3.3 ToPMine: mining phrases in general text -- 3.3.1 Frequent phrase mining -- 3.3.2 Segmentation and phrase filtering -- 3.3.3 Topical phrase ranking -- 3.4 Empirical analysis -- 3.4.1 The impact of the four criteria -- 3.4.2 Comparison of mining methods -- 3.4.3 Scalability -- | |
505 | 8 | _a4. Entity topical role analysis -- 4.1 Role of given entities -- 4.1.1 Entity specific phrase ranking -- 4.1.2 Distribution over subtopics -- 4.1.3 Case study -- 4.2 Entities of given roles -- | |
505 | 8 | _a5. Mining entity relations -- 5.1 Unsupervised hierarchical relation mining -- 5.1.1 Notations -- 5.1.2 Assumptions and framework -- 5.1.3 Stage 1: preprocessing -- 5.1.4 Stage 2: TPFG model -- 5.1.5 Model inference -- 5.1.6 Empirical analysis -- 5.2 Supervised hierarchical relation mining -- 5.2.1 Conditional random field for hierarchical relationship -- 5.2.2 Potential function design -- 5.2.3 Model inference and learning -- 5.2.4 Empirical analysis -- 5.3 Semi-supervised co-profiling -- 5.3.1 Observations -- 5.3.2 Model -- 5.3.3 Inference algorithm -- 5.3.4 Empirical analysis -- | |
505 | 8 | _a6. Scalable and robust topic discovery -- 6.1 Latent dirichlet allocation with topic tree -- 6.2 The STROD algorithm -- 6.2.1 Moment-based inference -- 6.2.2 Scalability improvement -- 6.2.3 Hyperparameter learning -- 6.3 Empirical analysis -- 6.3.1 Scalability -- 6.3.2 Robustness -- 6.3.3 Interpretability -- | |
505 | 8 | _a7. Application and research frontier -- 7.1 Application -- 7.1.1 Online analytical processing of information networks -- 7.1.2 Social influence and viral marketing -- 7.1.3 Relevance targeting -- 7.2 Research frontier -- | |
505 | 8 | _aBibliography -- Authors' biographies. | |
506 | 1 | _aAbstract freely available; full-text restricted to subscribers or individual document purchasers. | |
510 | 0 | _aCompendex | |
510 | 0 | _aINSPEC | |
510 | 0 | _aGoogle scholar | |
510 | 0 | _aGoogle book search | |
520 | 3 | _aThe 'big data' era is characterized by an explosion of information in the form of digital data collections, ranging from scientific knowledge, to social media, news, and everyone's daily life. Examples of such collections include scientific publications, enterprise logs, news articles, social media, and general web pages. Valuable knowledge about multi-typed entities is often hidden in the unstructured or loosely structured, interconnected data. Mining latent structures around entities uncovers hidden knowledge such as implicit topics, phrases, entity roles and relationships. In this monograph, we investigate the principles and methodologies of mining latent entity structures from massive unstructured and interconnected data. We propose a text-rich information network model for modeling data in many different domains. This leads to a series of new principles and powerful methodologies for mining latent structures, including (1) latent topical hierarchy, (2) quality topical phrases, (3) entity roles in hierarchical topical communities, and (4) entity relations. This book also introduces applications enabled by the mined structures and points out some promising research directions. | |
530 | _aAlso available in print. | ||
588 | _aTitle from PDF title page (viewed on April 26, 2015). | ||
650 | 0 | _aData mining. | |
650 | 0 | _aLatent structure analysis. | |
653 | _ainformation networks | ||
653 | _atext mining | ||
653 | _alink analysis | ||
653 | _atopic modeling | ||
653 | _aphrase extraction | ||
653 | _arole discovery | ||
653 | _aclustering | ||
653 | _aranking | ||
653 | _arelationship mining | ||
653 | _aprobabilistic models | ||
653 | _areal-world applications | ||
653 | _aefficient and scalable algorithms | ||
700 | 1 |
_aHan, Jiawei., _eauthor. |
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776 | 0 | 8 |
_iPrint version: _z9781627056601 |
830 | 0 | _aSynthesis digital library of engineering and computer science. | |
830 | 0 |
_aSynthesis lectures on data mining and knowledge discovery ; _v# 10. _x2151-0075 |
|
856 | 4 | 2 |
_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=7084069 |
999 |
_c562127 _d562127 |