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, while another explains the methods for quantitative summarization, including 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 issues such 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 modeling, 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, tied rankings median, consensus clustering, semi-average clustering, and 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.