May 1, 2024
Updated May 9, 2025
33 minute read
K-Means clustering is a fundamental algorithm in the field of unsupervised machine learning. At its core, K-Means attempts to partition a given dataset into a pre-determined number of distinct, non-overlapping subgroups or "clusters." The central idea is to group data points such that items within the same cluster exhibit greater similarity to one another than to items in other clusters. This technique is widely employed for its relative simplicity and effectiveness in discovering underlying patterns and structures within data.
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Reading list
We've selected 34 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
K-Means Clustering.
This practical guide is excellent for understanding how to implement K-Means clustering using popular Python libraries like Scikit-Learn. It focuses on practical application and provides concrete examples. must-read for anyone looking to apply K-Means in real-world scenarios and is widely used by industry professionals. The third edition is recently published.
Is specifically focused on unsupervised learning and clustering analysis, offering in-depth coverage of various clustering algorithms, including K-Means and its variations. It valuable resource for those specializing in clustering techniques and provides a deep dive into the subject.
Provides a comprehensive overview of statistical learning methods, including a detailed section on clustering techniques like K-Means. It foundational text for understanding the statistical basis of machine learning algorithms. While mathematically rigorous, it valuable reference for those looking to deepen their understanding of the theoretical aspects behind clustering.
Comprehensive overview of K-Means Clustering. It is written by a leading researcher in the field and is suitable for both beginners and advanced learners.
Comprehensive overview of K-Means Clustering, including its algorithms, applications, and extensions. It is written by leading researchers in the field and is suitable for both beginners and advanced learners.
Classic textbook on statistical learning, including a chapter on K-Means Clustering. It is written by leading researchers in the field and is suitable for both beginners and advanced learners.
A less mathematically intensive companion to 'The Elements of Statistical Learning,' this book offers a strong conceptual introduction to statistical learning, including clustering. It provides practical examples using R, making it suitable for undergraduate and early graduate students. is excellent for gaining a broad understanding and solidifying foundational concepts.
A recent publication focusing specifically on feature engineering using Scikit-Learn, which is highly relevant for improving the performance of K-Means and other clustering algorithms. It covers practical techniques and includes hands-on projects.
Provides a practical introduction to machine learning using Python and Scikit-Learn, with a section on clustering. It is well-suited for beginners and those with some programming experience looking to apply machine learning techniques. It helps solidify understanding through hands-on examples.
Offers a broad coverage of data mining concepts, with dedicated chapters on clustering, including K-Means. It provides a good balance of concepts and techniques, making it a valuable reference for both students and professionals. The fourth edition includes recent advancements in the field, including deep learning.
Feature engineering is crucial for effective clustering. provides a practical guide to creating and selecting features that can improve clustering performance. It includes examples relevant to preparing data for algorithms like K-Means.
Covers the mathematical and algorithmic foundations of data science, including topics relevant to clustering such as high-dimensional geometry and algorithms for massive datasets. It's suitable for advanced undergraduates and graduate students seeking a deeper theoretical understanding.
A comprehensive and advanced text that takes a probabilistic approach to machine learning, including a thorough treatment of clustering from this perspective. Suitable for advanced graduate students and researchers, it provides a deep theoretical understanding.
Comprehensive overview of machine learning, including a chapter on K-Means Clustering. It is written by a leading researcher in the field and is suitable for both beginners and advanced learners.
Comprehensive overview of machine learning, including a chapter on K-Means Clustering. It is written by leading researchers in the field and is suitable for both beginners and advanced learners.
Comprehensive overview of data mining, including a chapter on K-Means Clustering. It is written by leading researchers in the field and is suitable for both beginners and advanced learners.
Comprehensive overview of pattern recognition and clustering, including a chapter on K-Means Clustering. It is written by a leading researcher in the field and is suitable for both beginners and advanced learners.
Comprehensive overview of machine learning for data science, including a chapter on K-Means Clustering. It is written by leading researchers in the field and is suitable for both beginners and advanced learners.
Teaches data science concepts by implementing algorithms from scratch in Python, including clustering algorithms. It's great for understanding the underlying mechanics and for those who want to build a solid foundation without relying solely on libraries. The second edition is updated with new material.
A comprehensive and classic text covering the theoretical foundations of pattern recognition and machine learning, including clustering. It is mathematically rigorous and suitable for graduate students and researchers. provides a deep understanding of the probabilistic approaches underlying many clustering algorithms.
Practical guide to machine learning, including a chapter on K-Means Clustering. It is written by a leading researcher in the field and is suitable for both beginners and advanced learners.
Practical guide to machine learning using Python, including a chapter on K-Means Clustering. It is suitable for both beginners and advanced learners and is written by a leading researcher in the field.
Covers techniques for handling and analyzing massive datasets, including clustering algorithms designed for large-scale data. It's relevant for understanding the scalability aspects of K-Means and related methods when dealing with big data. The latest edition is recommended.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/lc6ct0/k
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