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Core Concepts in Data Analysis: Summarization, Correlation and Visualization (Undergraduate Topics in Computer Science) Paperback - 2011 - 2011th Edition
by Mirkin, Boris
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- Paperback
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Details
- Title Core Concepts in Data Analysis: Summarization, Correlation and Visualization (Undergraduate Topics in Computer Science)
- Author Mirkin, Boris
- Binding Paperback
- Edition number 2011th
- Edition 2011
- Condition Used - Good
- Pages 390
- Volumes 1
- Language ENG
- Publisher Springer
- Publication date 2011-04
- Illustrated Yes
- Bookseller's Inventory # 0857292862.G
- ISBN 9780857292865 / 0857292862
- Weight 1.26 lbs (0.57 kg)
- Dimensions 6.23 x 9.17 x 0.9 in (15.82 x 23.29 x 2.29 cm)
- Category Mathematics
- Library of Congress subjects Data structures (Computer science), Data structures (Computer science) -
- Library of Congress Catalogue Number 2011922052
- Dewey Decimal Code 006.312
- Quantity available 1
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From the publisher
From the rear cover
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.
