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001			286604
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008			150903s2013 xxk| o |||| 0|eng d
020			_a9781447148906 _99781447148906
024	7		_a10.1007/9781447148906 _2doi
035			_avtls000339949
039		9	_a201509030841 _bVLOAD _c201404300407 _dVLOAD _y201402061013 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA76.9.D343
100	1		_aCordeiro, Robson L. F. _eautor _9315335
245	1	0	_aData Mining in Large Sets of Complex Data / _cby Robson L. F. Cordeiro, Christos Faloutsos, Caetano Traina Júnior.
264		1	_aLondon : _bSpringer London : _bImprint: Springer, _c2013.
300			_axI, 116 páginas 37 ilustraciones, 25 ilustraciones en color. _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aSpringerBriefs in Computer Science, _x2191-5768
500			_aSpringer eBooks
505	0		_aPreface -- Introduction -- Related Work and Concepts -- Clustering Methods for Moderate-to-High Dimensionality Data -- Halite -- BoW -- QMAS -- Conclusion.
520			_aThe amount and the complexity of the data gathered by current enterprises are increasing at an exponential rate. Consequently, the analysis of Big Data is nowadays a central challenge in Computer Science, especially for complex data. For example, given a satellite image database containing tens of Terabytes, how can we find regions aiming at identifying native rainforests, deforestation or reforestation? Can it be made automatically? Based on the work discussed in this book, the answers to both questions are a sound “yes”, and the results can be obtained in just minutes. In fact, results that used to require days or weeks of hard work from human specialists can now be obtained in minutes with high precision. Data Mining in Large Sets of Complex Data discusses new algorithms that take steps forward from traditional data mining (especially for clustering) by considering large, complex datasets. Usually, other works focus in one aspect, either data size or complexity. This work considers both: it enables mining complex data from high impact applications, such as breast cancer diagnosis, region classification in satellite images, assistance to climate change forecast, recommendation systems for the Web and social networks; the data are large in the Terabyte-scale, not in Giga as usual; and very accurate results are found in just minutes. Thus, it provides a crucial and well timed contribution for allowing the creation of real time applications that deal with Big Data of high complexity in which mining on the fly can make an immeasurable difference, such as supporting cancer diagnosis or detecting deforestation.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aFaloutsos, Christos. _eautor _9313053
700	1		_aTraina Júnior, Caetano. _eautor _9315336
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781447148890
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-4471-4890-6 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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