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020 _a9783030002718
_9978-3-030-00271-8
024 7 _a10.1007/978-3-030-00271-8
_2doi
050 4 _aQ336
072 7 _aUN
_2bicssc
072 7 _aCOM021000
_2bisacsh
072 7 _aUN
_2thema
082 0 4 _a005.7
_223
100 1 _aMirkin, Boris.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aCore Data Analysis: Summarization, Correlation, and Visualization
_h[electronic resource] /
_cby Boris Mirkin.
250 _a2nd ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXV, 524 p. 187 illus., 80 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 _aUndergraduate Topics in Computer Science,
_x2197-1781
505 0 _aTopics in Data Analysis Substance -- Quantitative Summarization -- Learning Correlations -- Core Partitioning: K-Means and Similarity Clustering -- Divisive and Separate Cluster Structures -- Appendix. Basic Math and Code -- Index.
520 _aThis text examines the goals of data analysis with respect to enhancing knowledge, and identifies data summarization and correlation analysis as the core issues. Data summarization, both quantitative and categorical, is treated within the encoder-decoder paradigm bringing forward a number of mathematically supported insights into the methods and relations between them. Two Chapters describe methods for categorical summarization: partitioning, divisive clustering and separate cluster finding and another explain the methods for quantitative summarization, Principal Component Analysis and PageRank. Features: · An in-depth presentation of K-means partitioning including a corresponding Pythagorean decomposition of the data scatter. · Advice regarding such issues as clustering of categorical and mixed scale data, similarity and network data, interpretation aids, anomalous clusters, the number of clusters, etc. · Thorough attention to data-driven modelling including a number of mathematically stated relations between statistical and geometrical concepts including those between goodness-of-fit criteria for decision trees and data standardization, similarity and consensus clustering, modularity clustering and uniform partitioning. New edition highlights: · Inclusion of ranking issues such as Google PageRank, linear stratification and tied rankings median, consensus clustering, semi-average clustering, one-cluster clustering · Restructured to make the logics more straightforward and sections self-contained Core Data Analysis: Summarization, Correlation and Visualization is aimed at those who are eager to participate in developing the field as well as appealing to novices and practitioners. .
650 0 _aArtificial intelligence
_xData processing.
650 0 _aData protection.
650 0 _aData mining.
650 0 _aComputer science
_xMathematics.
650 1 4 _aData Science.
650 2 4 _aData and Information Security.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aMathematical Applications in Computer Science.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030002701
776 0 8 _iPrinted edition:
_z9783030002725
830 0 _aUndergraduate Topics in Computer Science,
_x2197-1781
856 4 0 _uhttps://doi.org/10.1007/978-3-030-00271-8
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c184769
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