Data Clustering: Theory, Algorithms, and Applications

最新推荐文章于 2024-10-11 15:02:27 发布

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最新推荐文章于 2024-10-11 15:02:27 发布

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本书从基本概念入手，介绍聚类分析及数据分类方法，并详细阐述了超过50种聚类算法，包括层次聚类、中心基聚类等。通过实际应用案例展示不同聚类架构与算法的优势和局限。

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Cluster analysis is an unsupervised process that divides a set of objects into homogeneous groups. This book starts with basic information on cluster analysis, including the classification of data and the corresponding similarity measures, followed by the presentation of over 50 clustering algorithms in groups according to some specific baseline methodologies such as hierarchical, center-based, and search-based methods. As a result, readers and users can easily identify an appropriate algorithm for their applications and compare novel ideas with existing results. The book also provides examples of clustering applications to illustrate the advantages and shortcomings of different clustering architectures and algorithms. Application areas include pattern recognition, artificial intelligence, information technology, image processing, biology, psychology, and marketing. Readers also learn how to perform cluster analysis with the C/C++ and MATLABÂ® programming languages
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