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003			BD-SySUS
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007			cr nn 008mamaa
008			130125s2013 xxk| s |||| 0|eng d
020			_a9781447148906 _9978-1-4471-4890-6
024	7		_a10.1007/978-1-4471-4890-6 _2doi
050		4	_aQA76.9.D343
072		7	_aUNF _2bicssc
072		7	_aUYQE _2bicssc
072		7	_aCOM021030 _2bisacsh
082	0	4	_a006.312 _223
100	1		_aCordeiro, Robson L. F. _eauthor.
245	1	0	_aData Mining in Large Sets of Complex Data _h[electronic resource] / _cby Robson L. F. Cordeiro, Christos Faloutsos, Caetano Traina Júnior.
264		1	_aLondon : _bSpringer London : _bImprint: Springer, _c2013.
300			_aXI, 116 p. 37 illus., 25 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		_aSpringerBriefs in Computer Science, _x2191-5768
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.
650		0	_aComputer science.
650		0	_aDatabase management.
650		0	_aData mining.
650	1	4	_aComputer Science.
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aDatabase Management.
700	1		_aFaloutsos, Christos. _eauthor.
700	1		_aTraina Júnior, Caetano. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781447148890
830		0	_aSpringerBriefs in Computer Science, _x2191-5768
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4471-4890-6
912			_aZDB-2-SCS
942			_2Dewey Decimal Classification _ceBooks
999			_c43685 _d43685