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001 978-0-85729-287-2
003 DE-He213
005 20140220083713.0
007 cr nn 008mamaa
008 110405s2011 xxk| s |||| 0|eng d
020 _a9780857292872
_9978-0-85729-287-2
024 7 _a10.1007/978-0-85729-287-2
_2doi
050 4 _aQA76.9.M35 
072 7 _aPBD
_2bicssc
072 7 _aUYAM
_2bicssc
072 7 _aCOM018000
_2bisacsh
072 7 _aMAT008000
_2bisacsh
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
912 _aZDB-2-SCS
999 _c105181
_d105181