Core Data Analysis: Summarization, Correlation, and Visualization

1. Front Matter

   Pages i-xv

   PDF

2. Topics in Substance of Data Analysis

   • Boris Mirkin

   Pages 1-75

3. Quantitative Summarization

   • Boris Mirkin

   Pages 77-161

4. Learning Correlations

   • Boris Mirkin

   Pages 163-292

5. Core Partitioning: K-means and Similarity Clustering

   • Boris Mirkin

   Pages 293-403

6. Divisive and Separate Cluster Structures

   • Boris Mirkin

   Pages 405-475

7. Back Matter

   Pages 477-524

   PDF

This text examines the goals of data analysis with respect to enhancing knowledge, and identifies data summarization and correlation analysis as the core issues. Data summarization, both quantitative and categorical, is treated within the encoder-decoder paradigm bringing forward a number of mathematically supported insights into the methods and relations between them. Two Chapters describe methods for categorical summarization: partitioning, divisive clustering and separate cluster finding and another explain the methods for quantitative summarization, Principal Component Analysis and PageRank.

Features:

·        An in-depth presentation of K-means partitioning including a corresponding Pythagorean decomposition of the data scatter.

·        Advice regarding such issues as clustering of categorical and mixed scale data, similarity and network data, interpretation aids, anomalous clusters, the number of clusters, etc.

·        Thorough attention to data-driven modelling including a number of mathematically stated relations between statistical and geometrical concepts including those between goodness-of-fit criteria for decision trees and data standardization, similarity and consensus clustering, modularity clustering and uniform partitioning.

New edition highlights:

·        Inclusion of ranking issues such as Google PageRank, linear stratification and tied rankings median, consensus clustering, semi-average clustering, one-cluster clustering

·        Restructured to make the logics more straightforward and sections self-contained

Core Data Analysis: Summarization, Correlation and Visualization is aimed at those who are eager to participate in developing the field as well as appealing to novices and practitioners. 

• Clustering
• Data Analysis
• K-means
• Principal component analysis
• Visualization
• data structures

“This book provides a clear overview of the data analysis process, the different types of statistical techniques employed for data analysis, and their role and purpose. … There is good use of a variety of examples to demonstrate how the different techniques are applied in practice. The book’s main purpose would be as a textbook for undergraduate students, or a reference book for data analysts.” (Mark Taylor, Computing Reviews, May 5, 2022)

• Department of Data Analysis and Artificial Intelligence, Faculty of Computer Science, National Research University Higher School of Economics, Moscow, Russia

  Boris Mirkin

Boris Mirkin holds a PhD in Computer Science (Mathematics) and DSc in Systems Analysis (Technology) degrees from Russian Universities. Between 1991-2010, he had long-term visiting appointments in France, Germany, USA, and a teaching appointment as a Professor of Computer Science at Birkbeck University of London, UK (2000-2010).

He develops methods for clustering and interpretation of complex data within the “data recovery” perspective.  Currently these approaches are being extended to automation of text analysis problems including the development and use of hierarchical ontologies. He has published a hundred  refereed papers and a dozen books, of which the latest are:  "Clustering: A Data Recovery Approach" (Chapman and Hall/CRC Press, 2012) and a textbook "Introductory Data Analysis" (In Russian, URAIT Publishers, Moscow, 2016). 

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