Cluster analysis basic concepts and algorithms book
K-Means Clustering Algorithm - Cluster Analysis - Machine Learning Algorithm - Data Science -Edureka
Gan G., Ma C., Wu J. Data Clustering: Theory, Algorithms, and Applications
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.
This chapter presents the basic concepts and methods of cluster analysis. In. Section , we . Scalability: Many clustering algorithms work well on small data sets containing fewer advanced topic and are not discussed in this book.
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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 Cambridge University Press, As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles.
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.