{"product_id":"data-clustering-with-python-from-theory-to-implementation-hardcover","title":"Data Clustering with Python: From Theory to Implementation - Hardcover","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eGuojun Gan\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eData 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.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eFeatures: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e \u003cli\u003eIntroduction to Python programming fundamentals\u003c\/li\u003e \u003cli\u003eOverview of key concepts in data clustering\u003c\/li\u003e \u003cli\u003eImplementation of popular clustering algorithms in Python\u003c\/li\u003e \u003cli\u003ePractical examples of applying clustering algorithms to datasets\u003c\/li\u003e \u003cli\u003eAccess to associated Python code on GitHub\u003c\/li\u003e \u003c\/ul\u003e\u003cp\u003eThis 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.\u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eGuojun Gan\u003c\/b\u003e 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.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 248\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.63 x 9.21 x 6.14 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e September 15, 2025\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":47337189507321,"sku":"9781032971568","price":179.8,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0789\/2782\/3097\/files\/7URw6Gdng29781032971568.webp?v=1769675509","url":"https:\/\/bookscloud.io\/products\/data-clustering-with-python-from-theory-to-implementation-hardcover","provider":"BooksCloud Book Dropshipping","version":"1.0","type":"link"}