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Core Concepts In Data Analysis Summarization Correlation And Visualization 1st Edition Boris Mirkin Auth

  • SKU: BELL-2148232
Core Concepts In Data Analysis Summarization Correlation And Visualization 1st Edition Boris Mirkin Auth
$ 31.00 $ 45.00 (-31%)

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Core Concepts In Data Analysis Summarization Correlation And Visualization 1st Edition Boris Mirkin Auth instant download after payment.

Publisher: Springer-Verlag London
File Extension: PDF
File size: 3.75 MB
Pages: 390
Author: Boris Mirkin (auth.)
ISBN: 9780857292865, 0857292862
Language: English
Year: 2011
Edition: 1

Product desciption

Core Concepts In Data Analysis Summarization Correlation And Visualization 1st Edition Boris Mirkin Auth by Boris Mirkin (auth.) 9780857292865, 0857292862 instant download after payment.

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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