Core Concepts in Data Analysis: Summarization, Correlation and Visualization

Nonfiction, Computers, Database Management, Data Processing, Science & Nature, Mathematics, Application Software
Cover of the book Core Concepts in Data Analysis: Summarization, Correlation and Visualization by Boris Mirkin, Springer London
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Author: Boris Mirkin ISBN: 9780857292872
Publisher: Springer London Publication: April 5, 2011
Imprint: Springer Language: English
Author: Boris Mirkin
ISBN: 9780857292872
Publisher: Springer London
Publication: April 5, 2011
Imprint: Springer
Language: English

Core Concepts in Data Analysis: Summarization, Correlation and Visualizationprovides 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.  

 The mathematical detail is encapsulated in the so-called “formulation” parts, whereas most material is delivered through “presentation” parts that explain the methods by applying them to small real-world data sets; concise “computation” parts inform of the algorithmic and coding issues.

 Four layers of active learning and self-study exercises are provided: worked examples, case studies, projects and questions.     

 

 

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

Core Concepts in Data Analysis: Summarization, Correlation and Visualizationprovides 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.  

 The mathematical detail is encapsulated in the so-called “formulation” parts, whereas most material is delivered through “presentation” parts that explain the methods by applying them to small real-world data sets; concise “computation” parts inform of the algorithmic and coding issues.

 Four layers of active learning and self-study exercises are provided: worked examples, case studies, projects and questions.     

 

 

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