Data Clustering With Python - From Theory to Implementation

Ideal for anyone interested in clustering algorithms, with no prior Python experience required.

List of contents

1. Python Programming 101. 2. The NumPy Library. 3. The Pandas Library. 4. The Matplotlib Library. 5. Introduction to Data Clustering. 6. Agglomerative Hierarchical Algorithms. 7. DIANA. 8. The k-means Algorithm. 9. The c-means Algorithm. 10. The k-prototypes Algorithm. 11. The Genetic k-modes Algorithm. 12. The FSC Algorithm. 13. The Gaussian Mixture Algorithm. 14 The KMTD Algorithm. 15. The Probability Propagation Algorithm. 16. A Spectral Clustering Algorithm. 17. A Mean-Shift Algorithm.

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.

Product details

• Authors: Guojun (University of Connecticut Gan, Gan Guojun
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Hardback
• Released: 10.10.2025
• EAN: 9781032971568
• ISBN: 978-1-0-3297156-8
• No. of pages: 248
• Series: Chapman & Hall/CRC The Python Series
• Subjects: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics; Data Mining, MATHEMATICS / Probability & Statistics / General, computer science, Probability & statistics, Mathematical & statistical software, Probability and statistics, Mathematical and statistical software, kernel-based;algorithms