Springer, 2015. — 420 p. — ISBN-10: 3319092588, ISBN-13: 978-3-319-09258-4.
На англ. языке.Clustering, the unsupervised classification of patterns into groups, is one of the most important tasks in exploratory data analysis. Primary goals of clustering include gaining insight into, classifying, and compressing data. Clustering has a long and rich history that spans a variety of scientific disciplines including anthropology, biology, medicine, psychology, statistics, mathematics, engineering, and computer science. As a result, numerous clustering algorithms have been proposed since the early 1950s. Among these algorithms, partitional (nonhierarchical) ones have found many applications, especially in engineering and computer science.This book focuses on partitional clustering algorithms, which are commonly used in engineering and computer scientific applications. The goal of this volume is to summarize the state-of-the-art in partitional clustering. This book provides coverage of consensus clustering, constrained clustering, large scale and/or high dimensional clustering, cluster validity, cluster visualization, and applications of clustering. The book includes such topics as center-based clustering, competitive learning clustering and density-based clustering. Each chapter is contributed by a leading expert in the field.Examines clustering as it applies to large and/or high-dimensional data sets commonly encountered in real-world applications.
Discusses algorithms specifically designed for partitional clustering.

Covers center-based, competitive learning, density-based, fuzzy, graph-based, grid-based, metaheuristic, and model-based approaches.

Springer, 2015. - 420页 - ISBN-10: 3319092588, ISBN-13: 978-319-09258-4.

读者可以通过以下方式来了解聚类。聚类是对模式进行无监督的分类，是探索性数据分析中最重要的任务之一。聚类的主要目标包括深入了解、分类和压缩数据。聚类有着悠久而丰富的历史，跨越了各种科学学科，包括人类学、生物学、医学、心理学、统计学、数学、工程学和计算机科学。因此，自20世纪50年代初以来，已经提出了许多聚类算法。在这些算法中，部分性（非层次性）的算法已经发现了许多应用，特别是在工程和计算机科学中。这本书的重点是部分性聚类算法，这些算法通常用于工程和计算机科学应用中。本卷的目标是总结部分条件聚类的最先进技术。本书提供了共识聚类、受限聚类、大规模和/或高维聚类、聚类有效性、聚类可视化和聚类应用的内容。本书包括基于中心的聚类、竞争学习聚类和基于密度的聚类等主题。每一章都是由该领域的顶尖专家贡献的。研究聚类，因为它适用于现实世界应用中经常遇到的大型和/或高维数据集。

讨论了专门为部分条件聚类设计的算法。

涵盖了基于中心的、竞争学习的、基于密度的、模糊的、基于图形的、基于网格的、元启发式的和基于模型的方法。