000			01681nam a22002055i 4500
999			_c33812 _d33812
001			nam a22 7a 4500
020			_a9783319730394
082			_a005.74 KO VI
100	1		_aKovalerchuk, Boris _917487
245	1	0	_aVisual knowledge discovery and machine learning _cBoris Kovalerchuk
260			_aUSA : _bSpringer, _cc2018.
300			_axxi, 317 p. : _bcol. ill. ; _c25 cm.
490	1		_aIntelligent Systems Reference Library, _x1868-4394 ; _v144
520			_aThis book combines the advantages of high-dimensional data visualization and machine learning in the context of identifying complex n-D data patterns. It vastly expands the class of reversible lossless 2-D and 3-D visualization methods, which preserve the n-D information. This class of visual representations, called the General Lines Coordinates (GLCs), is accompanied by a set of algorithms for n-D data classification, clustering, dimension reduction, and Pareto optimization. The mathematical and theoretical analyses and methodology of GLC are included, and the usefulness of this new approach is demonstrated in multiple case studies. These include the Challenger disaster, world hunger data, health monitoring, image processing, text classification, market forecasts for a currency exchange rate, computer-aided medical diagnostics, and others. As such, the book offers a unique resource for students, researchers, and practitioners in the emerging field of Data Science.
650		0	_aEngineering _9780
650		0	_aArtificial intelligence _9370
650		0	_aComputational intelligence _9907
856			_uhttps://uowd.box.com/s/emji2tyypl5bnk41qhxxpqodaug1myvj _zLocation Map
942			_2ddc _cREGULAR