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Overview: Cluster analysis is a fundamental data analysis task that aims to group similar data points together, revealing the inherent structure and patterns within complex datasets. This book serves as a comprehensive and accessible guide, taking readers on a captivating journey through the foundational principles of cluster analysis. The heart of the book is dedicated to a thorough examination of the various clustering algorithms, spanning partitioning methods, hierarchical methods, and more advanced techniques, such as mixture density-based clustering, graph clustering, and grid-based clustering. Each method is presented with a clear and concise explanation, accompanied by illustrative examples and hands-on implementations in the R programming language, a popular and powerful tool for data analysis and visualization. In this book, readers will become familiar with the foundational principles of cluster analysis, starting with an overview of Data Science and data mining, followed by a deep dive into the taxonomy of Machine Learning tasks. R is a free software programming language widely utilized by data scientists to develop data mining algorithms. The extensive and diverse range of contributed packages available in the Comprehensive R Archive Network (CRAN) allows most Data Science tasks to be efficiently completed with concise script code in R. For advanced undergraduate and graduate students, researchers and practitioners in the fields of Machine Learning, statistics, social sciences, data analysis, Data Science, data mining and bioinformatics.
Genre: Non-Fiction > Tech & Devices
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