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Clustering and Classification
At a moderately advanced level, this book seeks to cover the areas of clustering and related methods of data analysis where major advances are being made. Topics include: hierarchical clustering, variable selection and weighting, additive trees and other network models, relevance of neural network models to clustering, the role of computational complexity in cluster analysis, latent class approaches to cluster analysis, theory and method with applications of a hierarchical classes model in psychology and psychopathology, combinatorial data analysis, clusterwise aggregation of relations, review of the Japanese-language results on clustering, review of the Russian-language results on clustering and multidimensional scaling, practical advances, and significance tests.
The Social Media Survival Guide
A no-nonsense guide to the ever-evolving tools of social media, this handbook details the nuts and bolts of the open-source internet by using real-world examples with dozens of screen shots for each subject. The companion CD provides links to resources, and directories of social websites in addition to forms and worksheets designed to map social media strategies. This practical, hands-on introduction to social media tools such as Facebook, YouTube, and Twitter helps grow brand recognition, improve and expand sales, and increase profits for business owners, professionals, musicians, and artists alike.
Data Clustering with Python
Data clustering, an interdisciplinary field with diverse applications, has gained increasing popularity since its origins in the 1950s. Over the past six decades, researchers from various fields have proposed numerous clustering algorithms. In 2011, I wrote a book on implementing clustering algorithms in C++ using object-oriented programming. While C++ offers efficiency, its steep learning curve makes it less ideal for rapid prototyping. Since then, Python has surged in popularity, becoming the most widely used programming language since 2022. Its simplicity and extensive scientific libraries make it an excellent choice for implementing clustering algorithms. Features: Introduction to Python...
When Winning Costs Too Much
In this day and age when the sports pages of the local newspaper read like either a police report or a pharmacology text, it is impossible not to conclude that the mantra of winning has entered very dangerous ground. This book not only details these abuses and the dangers of the drugs themselves, but also addresses the misguided coaches, fialed mentors, and poor role models who have contributed to the decline of the sports-for-sports sake mentalitly.
Dope
Since the dawn of athletic competition during the original Olympic Games in Ancient Greece, athletes, as well as their coaches and trainers, have been finding innovative ways to gain an edge on their competition. Some of those performance-enhancement methods have been within the accepted rules while other methods skirt the gray area between being within the rules and not, while still other methods break the established rules. In modern times, doping - the use of performance-enhancing drugs - has been one method athletes and their trainers have used to beat their competition. The history of sports doping during the modern era can be traced through the events and scandals of the times in which...
Hypothesis Generation and Interpretation
This book focuses in detail on data science and data analysis and emphasizes the importance of data engineering and data management in the design of big data applications. The author uses patterns discovered in a collection of big data applications to provide design principles for hypothesis generation, integrating big data processing and management, machine learning and data mining techniques. The book proposes and explains innovative principles for interpreting hypotheses by integrating micro-explanations (those based on the explanation of analytical models and individual decisions within them) with macro-explanations (those based on applied processes and model generation). Practical case ...
Data Clustering in C++
Data clustering is a highly interdisciplinary field, the goal of which is to divide a set of objects into homogeneous groups such that objects in the same group are similar and objects in different groups are quite distinct. Thousands of theoretical papers and a number of books on data clustering have been published over the past 50 years. However, few books exist to teach people how to implement data clustering algorithms. This book was written for anyone who wants to implement or improve their data clustering algorithms. Using object-oriented design and programming techniques, Data Clustering in C++ exploits the commonalities of all data clustering algorithms to create a flexible set of re...
St. Nicholas
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