Cluster Analysis: A Primer Using R
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A01=Lior Rokach
Age Group_Uncategorized
Age Group_Uncategorized
Agglomerative Clustering
Author_Lior Rokach
automatic-update
Big Data Analysis
Category1=Non-Fiction
Category=UNC
Category=UNF
Cluster Analysis
Clustering Algorithms
Clustering Evaluation
COP=Singapore
Data Clustering
Data Mining
Data Science
Data Visualization
DBSCAN
Deep Learning
Delivery_Pre-order
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eq_computing
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eq_isMigrated=2
eq_nobargain
eq_non-fiction
Graph Clustering
Grid-Based Clustering
Hierarchical Clustering
K-Means Clustering
Language_English
Machine Learning
Mixture Models
PA=Temporarily unavailable
Partitioning Methods
Predictive Analytics
Price_€100 and above
PS=Forthcoming
R Programming
Similarity Measures
softlaunch
Spectral Clustering
Statistical Analysis
Unsupervised Learning
Product details
- ISBN 9789811297472
- Publication Date: 28 Oct 2024
- Publisher: World Scientific Publishing Co Pte Ltd
- Publication City/Country: SG
- Product Form: Hardback
- Language: English
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Cluster analysis is a fundamental data analysis task that aims to group similar data points together, revealing the inherent structure and patterns within complex datasets. This book serves as a comprehensive and accessible guide, taking readers on a captivating journey through the foundational principles of cluster analysis.At its core, the book delves deeply into various clustering algorithms, covering partitioning methods, hierarchical methods, and advanced techniques such as mixture density-based clustering, graph clustering, and grid-based clustering. Each method is presented with clear, concise explanations, supported by illustrative examples and hands-on implementations in the R programming language — a popular and powerful tool for data analysis and visualization.Recognizing the importance of cluster validation and evaluation, the book devotes a dedicated chapter to exploring a wide range of internal and external quality criteria, equipping readers with the necessary tools to assess the performance of clustering algorithms. For those eager to stay at the forefront of the field, the book also presents deep learning-based clustering methods, showcasing the remarkable capabilities of neural networks in uncovering hidden structures within complex, high-dimensional data.Whether you are a student seeking to expand your knowledge, a data analyst looking to enhance your toolbox, or a researcher exploring the frontiers of data analysis, this book will provide you with a solid foundation in cluster analysis and empower you to tackle a wide range of data-driven problems.
