© 2011

Core Concepts in Data Analysis: Summarization, Correlation and Visualization


Part of the Undergraduate Topics in Computer Science book series (UTICS)

Table of contents

  1. Front Matter
    Pages i-xx
  2. Boris Mirkin
    Pages 1-30
  3. Boris Mirkin
    Pages 113-172
  4. Boris Mirkin
    Pages 173-219
  5. Boris Mirkin
    Pages 221-281
  6. Boris Mirkin
    Pages 283-313
  7. Back Matter
    Pages 357-390

About this book


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.     




Clustering Data Analysis K-means Principal component analysis Visualization

Authors and affiliations

  1. 1., Department of Computer ScienceUniversity of LondonLondonUnited Kingdom

Bibliographic information

Industry Sectors
IT & Software


From the reviews:

“Oriented toward undergraduate students in the computer science field, this work offers a unique approach to data analysis by focusing primarily on summarization, correlation, and visualization techniques instead of more broad-based approaches. Summarization is the more prevalent topic in this book, with detailed coverage of clustering and principal component analysis--two important areas of summarization often treated as heuristics. … Summing Up: Highly recommended. Upper-division undergraduates and faculty.” (D. J. Gougeon, Choice, Vol. 49 (2), October, 2011)

“This textbook follows an unconventional way to present the main aspects regarding data analysis. … the reader is led in a friendly way through different data analysis areas … . this book represents an exciting text, covering the main topics of the data analysis area. It can be successfully used as a textbook for BS and MS students in computer science, on the one hand, and for researchers in data mining and related fields, on the other hand.” (Florin Gorunescu, Zentralblatt MATH, Vol. 1219, 2011)

“Core concepts in data analysis is clean and devoid of any fuzziness. The author presents his theses with a refreshing clarity seldom seen in a text of this sophistication. The entire text is rich in solved examples, case studies, projects, and introspective questions. … To single out just one of the text’s many successes: I doubt readers will ever encounter again such a detailed and excellent treatment of correlation concepts. … statisticians will also find it refreshing and engaging.” (James Van Speybroeck, ACM Computing Reviews, June, 2011)