LEADER 04067nam 22006255 450 001 9910337856403321 005 20230810133552.0 010 $a3-030-00271-3 024 7 $a10.1007/978-3-030-00271-8 035 $a(CKB)4100000007938079 035 $a(DE-He213)978-3-030-00271-8 035 $a(MiAaPQ)EBC5921823 035 $a(PPN)235669229 035 $a(EXLCZ)994100000007938079 100 $a20190415d2019 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aCore Data Analysis: Summarization, Correlation, and Visualization /$fby Boris Mirkin 205 $a2nd ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (XV, 524 p. 187 illus., 80 illus. in color.) 225 1 $aUndergraduate Topics in Computer Science,$x2197-1781 311 $a3-030-00270-5 327 $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. 330 $aThis text examines the goals of data analysis with respect to enhancing knowledge, and identifies data summarization and correlation analysis as the core issues. Data summarization, both quantitative and categorical, is treated within the encoder-decoder paradigm bringing forward a number of mathematically supported insights into the methods and relations between them. Two Chapters describe methods for categorical summarization: partitioning, divisive clustering and separate cluster finding and another explain the methods for quantitative summarization, Principal Component Analysis and PageRank. Features: · An in-depth presentation of K-means partitioning including a corresponding Pythagorean decomposition of the data scatter. · Advice regarding such issues as clustering of categorical and mixed scale data, similarity and network data, interpretation aids, anomalous clusters, the number of clusters, etc. · Thorough attention to data-driven modelling including a number of mathematically stated relations between statistical and geometrical concepts including those between goodness-of-fit criteria for decision trees and data standardization, similarity and consensus clustering, modularity clustering and uniform partitioning. New edition highlights: · Inclusion of ranking issues such as Google PageRank, linear stratification and tied rankings median, consensus clustering, semi-average clustering, one-cluster clustering · Restructured to make the logics more straightforward and sections self-contained Core Data Analysis: Summarization, Correlation and Visualization is aimed at those who are eager to participate in developing the field as well as appealing to novices and practitioners. . 410 0$aUndergraduate Topics in Computer Science,$x2197-1781 606 $aArtificial intelligence$xData processing 606 $aData protection 606 $aData mining 606 $aComputer science$xMathematics 606 $aData Science 606 $aData and Information Security 606 $aData Mining and Knowledge Discovery 606 $aMathematical Applications in Computer Science 615 0$aArtificial intelligence$xData processing. 615 0$aData protection. 615 0$aData mining. 615 0$aComputer science$xMathematics. 615 14$aData Science. 615 24$aData and Information Security. 615 24$aData Mining and Knowledge Discovery. 615 24$aMathematical Applications in Computer Science. 676 $a519.50285 676 $a519.535 700 $aMirkin$b Boris$4aut$4http://id.loc.gov/vocabulary/relators/aut$01057911 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910337856403321 996 $aCore Data Analysis: Summarization, Correlation, and Visualization$92495429 997 $aUNINA