Data Clustering with Python: From Theory to Implementation
About this ebook
Features:
- Introduction to Python programming fundamentals
- Overview of key concepts in data clustering
- Implementation of popular clustering algorithms in Python
- Practical examples of applying clustering algorithms to datasets
- Access to associated Python code on GitHub
This book extends my previous work by implementing clustering algorithms in Python. Unlike the object-oriented approach in C++, this book uses a procedural programming style, as Python allows many clustering algorithms to be implemented concisely. The book is divided into two parts: the first introduces Python and key libraries like NumPy, Pandas, and Matplotlib, while the second covers clustering algorithms, including hierarchical and partitional methods. Each chapter includes theoretical explanations, Python implementations, and practical examples, with comparisons to scikit-learn where applicable.
This book is ideal for anyone interested in clustering algorithms, with no prior Python experience required. The complete source code is available at: https://github.com/ganml/dcpython.
About the author
Guojun Gan is an Associate Professor in the Department of Mathematics at the University of Connecticut, where he has been since August 2014. Prior to that, he worked at a large life insurance company in Toronto, Canada for six years and a hedge fund in Oakville, Canada for one year. He earned a BS degree from Jilin University, Changchun, China, in 2001 and MS and PhD degrees from York University, Toronto, Canada, in 2003 and 2007, respectively.
