Core Concepts In Data Analysis Summarization Correlation And Visualization Pdf

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core concepts in data analysis summarization correlation and visualization pdf

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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 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.

Core Concepts in Data Analysis: Summarization, Correlation and Visualization

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Mirkin Published in Undergraduate Topics in…. 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 and network clustering or correlate different aspects of data decision trees, linear rules, neuron networks, and Bayes rule.

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Core Concepts in Data Analysis: Summarization, Correlation

Proceedings Lecture Notes in Computer Science and General Issues. May 17, May 10, April 16, Discrete Mathematics. Advanced techniques for factoring and extracting discrete logarithms.

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 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. Four layers of active learning and self-study exercises are provided: worked examples, case studies, projects and questions. Summarization is the more prevalent topic in this book, with detailed coverage of clustering and principal component analysis--two important areas of summarization often treated as heuristics. Upper-division undergraduates and faculty.

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