Core concepts in data analysis : summarization, correlation and visualization

Resource Information

The work Core concepts in data analysis : summarization, correlation and visualization represents a distinct intellectual or artistic creation found in University of Missouri Libraries. This resource is a combination of several types including: Work, Language Material, Books.

Label

Core concepts in data analysis : summarization, correlation and visualization

Title remainder

summarization, correlation and visualization

Statement of responsibility

Boris Mirkin

Creator

• Mirkin, B. G., (Boris Grigorʹevich)

Subject

• Data mining
• Data mining
• Informatique
• Mathematical statistics -- Data processing
• Mathematical statistics -- Data processing
• Mathematical statistics -- Data processing
• Data mining

Language

eng

Summary

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.

Member of

• Undergraduate topics in computer science

Cataloging source

GW5XE

Dewey number

519.5/35

Index

index present

LC call number

QA276.4

LC item number

.M57 2011

Literary form

non fiction

Nature of contents

• dictionaries
• bibliography

Series statement

Undergraduate topics in computer science