| 书目名称 | Metaheuristic Clustering | | 编辑 | Swagatam Das,Ajith Abraham,Amit Konar | | 视频video | | | 概述 | Latest research on metaheuristic clustering | | 丛书名称 | Studies in Computational Intelligence | | 图书封面 |  | | 描述 | .Cluster analysis means the organization of an unlabeled collection of objects or patterns into separate groups based on their similarity. The task of computerized data clustering has been approached from diverse domains of knowledge like graph theory, multivariate analysis, neural networks, fuzzy set theory, and so on. Clustering is often described as an unsupervised learning method but most of the traditional algorithms require a prior specification of the number of clusters in the data for guiding the partitioning process, thus making it not completely unsupervised. Modern data mining tools that predict future trends and behaviors for allowing businesses to make proactive and knowledge-driven decisions, demand fast and fully automatic clustering of very large datasets with minimal or no user intervention. ..In this volume, we formulate clustering as an optimization problem, where the best partitioning of a given dataset is achieved by minimizing/maximizing one (single-objective clustering) or more (multi-objective clustering) objective functions. Using several real world applications, we illustrate the performance of several metaheuristics, particularly the Differential Evolutio | | 出版日期 | Book 2009 | | 关键词 | algorithms; data mining; evolution; heuristics; kernel; knowledge; learning; metaheuristic; modeling; neural | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-540-93964-1 | | isbn_softcover | 978-3-642-10071-0 | | isbn_ebook | 978-3-540-93964-1Series ISSN 1860-949X Series E-ISSN 1860-9503 | | issn_series | 1860-949X | | copyright | Springer-Verlag Berlin Heidelberg 2009 |
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发表于 2025-3-21 18:21:11
