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

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Core Concepts in Data Analysis: Summarization, Correlation and Visualization

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Learning Multivariate Correlations in Data,te measures described in Chapter 3– Quetelet indexes in contingency tables, first of all – and, second, normalization options for dummy variables representing target categories. Some related concepts such as Bayes decision rule, bag-of-word model in text analysis, VC-complexity and kernel for non-linear classification are introduced too.

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Textbook 20111st editioneither 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 multi

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Ling Shi,Lihua Xie,Richard M. Murrayot not. This difference is somewhat blurred at the binary features representing individual categories. They can be represented by the so-called dummy variables that can be considered quantitative too. Contemporary approaches, nature inspired optimization and bootstrap validation, are explained on individual cases.

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Payam Naghshtabrizi,Jo?o P. Hespanhaeatures rather than postulates it. Two more distant applications of PCA, Latent semantic analysis (for disambiguation in document retrieval) and Correspondence analysis (for visualization of contingency tables), are explained too. The issue of data standardization in data summarization problems, remaining unsolved, is discussed at length.

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Guandong Xu,Yanchun Zhang,Lin Lilits 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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