Cluster analysis basic concepts and algorithms book
Introduction to Data Mining, Global Edition : Pang-Ning Tan :
This book provides the reader with a basic understanding of the formal concepts of the cluster, clustering, partition, cluster analysis etc. The book explains feature-based, graph-based and spectral clustering methods and discusses their formal similarities and differences. Understanding the related formal concepts is particularly vital in the epoch of Big Data; due to the volume and characteristics of the data, it is no longer feasible to predominantly rely on merely viewing the data when facing a clustering problem. Usually clustering involves choosing similar objects and grouping them together. To facilitate the choice of similarity measures for complex and big data, various measures of object similarity, based on quantitative like numerical measurement results and qualitative features like text , as well as combinations of the two, are described, as well as graph-based similarity measures for hyper linked objects and measures for multilayered graphs. Numerous variants demonstrating how such similarity measures can be exploited when defining clustering cost functions are also presented. In addition, the book provides an overview of approaches to handling large collections of objects in a reasonable time.
K-means clustering: how it works
Introduction to Data Mining, Global Edition
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Society for Industrial and Applied Mathematics, , pp. Springer, Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist
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Clustering: K-means and Hierarchical
It seems that you're in Germany. We have a dedicated site for Germany. Authors: Wierzchon , Slawomir, Klopotek , Mieczyslaw. This book provides the reader with a basic understanding of the formal concepts of the cluster, clustering, partition, cluster analysis etc. The book explains feature-based, graph-based and spectral clustering methods and discusses their formal similarities and differences. Understanding the related formal concepts is particularly vital in the epoch of Big Data; due to the volume and characteristics of the data, it is no longer feasible to predominantly rely on merely viewing the data when facing a clustering problem. Usually clustering involves choosing similar objects and grouping them together.
By using our site, you acknowledge that you have read and understand our Cookie Policy , Privacy Policy , and our Terms of Service. Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. It only takes a minute to sign up. I'm searching for books on the basic k-means and divisive clustering algorithms. I'm interested in the pros and cons of both. It's a part of my bachelors thesis, I have implemented both and need books to create my used literature list for the theoretical part.
Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms. This book provides a comprehensive coverage of important data mining techniques. Numerous examples are provided to lucidly illustrate the key concepts.
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Cluster Analysis: Basic Concepts and Algorithms . the bibliographic notes provide references to relevant books and papers that explore cluster.
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