Core Concepts in Data Analysis: Summarization, Correlation and Visualization

Mirkin, Boris

ISBN 10: 0857292862 / ISBN 13: 9780857292865

Editore: Springer, 2011

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This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,700grams, ISBN:9780857292865.

Codice articolo 3956032

Dati bibliografici

• Titolo: Core Concepts in Data Analysis: Summarization, Correlation and Visualization
• Editore: Springer
• Data di pubblicazione: 2011
• Lingua: Inglese
• ISBN 10: 0857292862
• ISBN 13: 9780857292865
• Rilegatura: Brossura
• Condizione: Good
• Peso: 700 grammi
• Cataloghi dei venditori:
  • Science & Technology

Descrizione articolo

Riassunto

With in-depth descriptions of data analysis techniques both for summarizing and correlation, the author’s unconventional approach employs the concept of multivariate data summarization as an alternative to conventional machine-learning prediction methods.

Dalla quarta di copertina

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.  

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.