000			03369nam a22004815i 4500
001			978-0-85729-287-2
003			DE-He213
005			20251006084443.0
007			cr nn 008mamaa
008			110405s2011 xxk| s |||| 0|eng d
020			_a9780857292872
020			_a99780857292872
024	7		_a10.1007/978-0-85729-287-2 _2doi
082	0	4	_a004.0151 _223
100	1		_aMirkin, Boris. _eauthor.
245	1	0	_aCore Concepts in Data Analysis: Summarization, Correlation and Visualization _h[electronic resource] / _cby Boris Mirkin.
264		1	_aLondon : _bSpringer London : _bImprint: Springer, _c2011.
300			_aXX, 390p. 129 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aUndergraduate Topics in Computer Science, _x1863-7310
520			_aCore Concepts in Data Analysis: Summarization, Correlation and Visualization provides in-depth descriptions of those data analysis approaches that either summarize data (principal component analysis and clustering, including hierarchical and network clustering) or correlate different aspects of data (decision trees, linear rules, neuron networks, and Bayes rule). Boris Mirkin takes an unconventional approach and introduces the concept of multivariate data summarization as a counterpart to conventional machine learning prediction schemes, utilizing techniques from statistics, data analysis, data mining, machine learning, computational intelligence, and information retrieval. Innovations following from his in-depth analysis of the models underlying summarization techniques are introduced, and applied to challenging issues such as the number of clusters, mixed scale data standardization, interpretation of the solutions, as well as relations between seemingly unrelated concepts: goodness-of-fit functions for classification trees and data standardization, spectral clustering and additive clustering, correlation and visualization of contingency data.   The mathematical detail is encapsulated in the so-called "formulation" parts, whereas most material is delivered through "presentation" parts that explain the methods by applying them to small real-world data sets; concise "computation" parts inform of the algorithmic and coding issues. Four layers of active learning and self-study exercises are provided: worked examples, case studies, projects and questions.
650		0	_aCOMPUTER SCIENCE.
650		0	_aCOMPUTATIONAL COMPLEXITY.
650		0	_aARTIFICIAL INTELLIGENCE.
650		0	_aOPTICAL PATTERN RECOGNITION.
650	1	4	_aCOMPUTER SCIENCE.
650	2	4	_aDISCRETE MATHEMATICS IN COMPUTER SCIENCE.
650	2	4	_aPROBABILITY AND STATISTICS IN COMPUTER SCIENCE.
650	2	4	_aMATH APPLICATIONS IN COMPUTER SCIENCE.
650	2	4	_aARTIFICIAL INTELLIGENCE (INCL. ROBOTICS).
650	2	4	_aPATTERN RECOGNITION.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780857292865
830		0	_aUndergraduate Topics in Computer Science, _x1863-7310
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-85729-287-2 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SCS
942			_2ddc _cER
999			_c60005 _d60005