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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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 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.
Covers the fundamental principles of data mining, with a section dedicated to clustering. It provides a good theoretical foundation and discusses various data mining tasks. It's a valuable reference for understanding the broader context of clustering within data mining.
A classic textbook in machine learning that covers fundamental concepts and algorithms, including clustering. While older, it provides a strong theoretical foundation and historical context for the field. It's a valuable reference for understanding the origins of many ML techniques.
While focusing on predictive modeling, this book covers essential data preprocessing and feature engineering techniques relevant to clustering. It provides practical guidance and case studies, valuable for professionals. It offers a good perspective on how clustering fits into a larger modeling workflow.
While focused on text analysis, this book demonstrates how clustering, including K-Means, can be applied to text data. It provides practical examples using Python and relevant libraries. Useful for those interested in applying clustering to unstructured data.
This introductory text to data science includes coverage of exploratory data analysis and unsupervised learning techniques like clustering. It's suitable for beginners and provides a broad overview of the data science workflow where clustering plays a role.
Focuses on the statistical concepts essential for data science, including those relevant to understanding and evaluating clustering results. It's a practical guide for data scientists needing to strengthen their statistical background. It helps in interpreting the output of clustering algorithms like K-Means.
Focuses on the practical aspects of deploying and maintaining machine learning systems. While not solely about clustering, it provides valuable context on integrating clustering models into production environments. It's relevant for professionals working with ML systems.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/lc6ct0/k
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