Data Clustering with Python: From Theory to Implementation
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Cover....1 Half Title....2 Series Page....3 Title Page....4 Copyright Page....5 Dedication....6 Contents....8 Preface....12 I. Python Programming Preliminaries....14 1. Python Programming 101....16 1.1. Installation....16 1.2. Variables and data types....20 1.3. Data structures....24 1.4. Operators....28 1.5. Control statements and loops....34 1.6. Functions....37 1.7. File IO....38 1.8. Error handling....41 1.9. Object-oriented programming....42 1.10. Code Optimization....43 1.11. Summary....45 2. The NumPy Library....46 2.1. Arrays....46 2.2. Array indexing and slicing....49 2.3. Views and copies....50 2.4. Array operations....52 2.5. Functions....53 2.6. Matrices....56 2.7. File IO....59 2.8. Code optimization....60 2.9. Summary....62 3. The Pandas Library....63 3.1. Pandas series....63 3.2. Pandas data frames....66 3.3. Views and copies....69 3.4. Data manipulation....71 3.5. File IO....76 3.6. Summary....78 4. The Matplotlib Library....79 4.1. Overview....79 4.2. Basic plotting....81 4.3. Subplots....84 4.4. File IO....86 4.5. Summary....86 II. Data Clustering in Python....88 5. Introduction to Data Clustering....90 5.1. History of Clustering....90 5.2. Data Clustering Process....92 5.3. Clusters....94 5.4. Data Types....95 5.5. Dissimilarity and Similarity Measures....97 5.5.1. Measures for Continuous Data....97 5.5.2. Measures for Discrete Data....98 5.5.3. Measures for Mixed-type Data....99 5.6. Hierarchical Clustering Algorithms....100 5.6.1. Agglomerative Hierarchical Algorithms....100 5.6.2. Divisive Hierarchical Algorithms....103 5.6.3. Other Hierarchical Algorithms....103 5.6.4. Dendrograms....103 5.7. Partitional Clustering Algorithms....104 5.7.1. Center-Based Clustering Algorithms....106 5.7.2. Search-based Clustering Algorithms....107 5.7.3. Graph-Based Clustering Algorithms....107 5.7.4. Grid-Based Clustering Algorithms....108 5.7.5. Density-Based Clustering Algorithms....108 5.7.6. Model-Based Clustering Algorithms....109 5.7.7. Subspace Clustering Algorithms....110 5.7.8. Neural Network-Based Clustering Algorithms....110 5.7.9. Fuzzy Clustering Algorithms....111 5.8. Cluster Validity....111 5.9. Clustering Applications....112 5.10. Literature on Data Clustering....112 5.11. Summary....116 6. Agglomerative Hierarchical Algorithms....117 6.1. Description of the Algorithm....117 6.2. Implementation....119 6.2.1. The Single Linkage Algorithm....121 6.2.2. The Complete Linkage Algorithm....122 6.2.3. The Group Average Algorithm....123 6.2.4. The Weighted Group Average Algorithm....124 6.2.5. The Centroid Algorithm....125 6.2.6. The Median Algorithm....126 6.2.7. Ward’s Algorithm....127 6.3. Examples....129 6.4. Summary....135 7. A Divisive Hierarchical Clustering Algorithm....137 7.1. Description of the Algorithm....137 7.2. Implementation....138 7.3. Examples....140 7.4. Summary....142 8. The k-means Algorithm....143 8.1. Description of the Algorithm....143 8.2. Implementation....144 8.3. Examples....146 8.4. Summary....150 9. The c-means Algorithm....152 9.1. Description of the Algorithm....152 9.2. Implementation....153 9.3. Examples....155 9.4. Summary....160 10. The k-prototypes Algorithm....161 10.1. Description of the Algorithm....161 10.2. Implementation....162 10.3. Examples....165 10.4. Summary....169 11. The Genetic k-modes Algorithm....170 11.1. Description of the Algorithm....170 11.2. Implementation....172 11.3. Examples....174 11.4. Summary....176 12. The FSC Algorithm....177 12.1. Description of the Algorithm....177 12.2. Implementation....179 12.3. Examples....181 12.4. Summary....185 13. The Gaussian Mixture Algorithm....187 13.1. Description of the Algorithm....187 13.2. Implementation....190 13.3. Examples....191 13.4. Summary....195 14. The KMTD Algorithm....196 14.1. Description of the Algorithm....196 14.2. Implementation....199 14.3. Examples....201 14.4. Summary....204 15. The Probability Propagation Algorithm....205 15.1. Description of the Algorithm....205 15.2. Implementation....207 15.3. Examples....208 15.4. Summary....212 16. A Spectral Clustering Algorithm....213 16.1. Description of the Algorithm....213 16.2. Implementation....214 16.3. Examples....215 16.4. Summary....222 17. A Mean-Shift Algorithm....223 17.1. Description of the Algorithm....223 17.2. Implementation....225 17.3. Examples....227 17.4. Summary....234 Bibliography....236 Index....258
Описание
В этом материале разберём тему: clustering.
Over the past six decades, researchers from various fields have proposed numerous clustering algorithms. Data clustering, an interdisciplinary field with diverse applications, has gained increasing popularity since its origins in the 1950s. In 2011, I wrote a book on implementing clustering algorithms in C++ using object-oriented programming. Since then, Python has surged in popularity, becoming the most widely used programming language since 2022. While C++ offers efficiency, its steep learning curve makes it less ideal for rapid prototyping. Its simplicity and extensive scientific libraries make it an excellent choice for implementing clustering algorithms.
Unlike the object-oriented approach in C++, this book uses a procedural programming style, as Python allows many clustering algorithms to be implemented concisely. Features:Introduction to Python programming fundamentalsOverview of key concepts in data clusteringImplementation of popular clustering algorithms in PythonPractical examples of applying clustering algorithms to datasetsAccess to associated Python code on GitHubThis book extends my previous work by implementing clustering algorithms in Python. 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.
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автор — Gan Guojun, издательство CRC Press is an imprint of Taylor & Francis Group, LLC, год выпуска 2026, 260 страниц.
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Data clustering, an interdisciplinary field with diverse applications, has gained increasing popularity since its origins in the 1950s.
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