Titlebook: Core Concepts in Data Analysis: Summarization, Correlation and Visualization; Boris Mirkin Textbook 20111st edition Springer-Verlag London

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Hierarchical Clustering,lits conceptually, that is, using one feature at a time. The last section is devoted to the Single Link clustering, a popular method for extraction of elongated structures from the data. Relations between single link clustering and two popular graph-theoretic structures, the Minimum Spanning Tree (MST) and connected components, are explained.

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Annalisa Bonfiglio,Danilo De Rossiata analysis problems is presented. The datasets are taken from various fields such as monitoring market towns, computer security protocols, bioinformatics, cognitive psychology. (iii)An overview of data visualization, its goals and some techniques is given.

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Introduction: What Is Core,ata analysis problems is presented. The datasets are taken from various fields such as monitoring market towns, computer security protocols, bioinformatics, cognitive psychology. (iii)An overview of data visualization, its goals and some techniques is given.

Processes

2D Analysis: Correlation and Visualization of Two Features,dence, and Pearson’s chi-squared for two nominal variables; the latter is treated as a summary correlation measure, in contrast to the conventional view of it as a criterion of statistical independence. They all are applicable in the case of multidimensional data as well.

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1863-7310 d to date..Explores methodical innovations of summarization .Core 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

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https://doi.org/10.1007/978-0-85729-287-2Clustering; Data Analysis; K-means; Principal component analysis; Visualization