Boris Mirkin Libri

Focusing on clustering, this monograph by Boris Mirkin presents a comprehensive compilation of research, including original Russian developments previously unseen internationally. It explores a range of applications across various fields such as biology, sociology, and organizational structures. Mirkin's innovative approach, approximation clustering, systematizes the discipline and introduces new optimization methods relevant to data analysis. The book appeals to specialists and students alike, making it a valuable resource in multiple scientific domains.

With in-depth descriptions of data analysis techniques both for summarizing and correlation, the author's unconventional approach employs the concept of multivariate data summarization as an alternative to conventional machine- learning prediction methods.

Focusing on clustering methods, the book explores both classical and modern techniques, including K-Means and divisive clustering. Enhanced case studies illustrate practical applications, while the second edition introduces new topics such as variable selection, similarity data clustering, and spectral clustering. Additionally, it offers resources through a dedicated website featuring MATLAB code and datasets for the examples discussed, making it a comprehensive guide for understanding and implementing clustering techniques.

The book presents an innovative perspective on data analysis techniques, emphasizing multivariate data summarization as a viable alternative to traditional machine-learning prediction methods. It offers detailed insights into various methods for summarizing data and exploring correlations, challenging conventional approaches and encouraging readers to rethink their analytical strategies.