| 书目名称 | Data Analysis in Bi-partial Perspective: Clustering and Beyond | | 编辑 | Jan W. Owsiński | | 视频video | | | 概述 | Offers a valuable resource for all data scientists who wish to broaden their perspective on the fundamental approaches available.Presents a general formulation, properties, examples, and techniques as | | 丛书名称 | Studies in Computational Intelligence | | 图书封面 |  | | 描述 | .This book presents the .bi-partial approach. to data analysis, which is both uniquely general and enables the development of techniques for many data analysis problems, including related models and algorithms. It is based on adequate representation of the essential clustering problem: .to group together the similar, and to separate the dissimilar.. This leads to a general objective function and subsequently to a broad class of concrete implementations. Using this basis, a suboptimising procedure can be developed, together with a variety of implementations..This procedure has a striking affinity with the classical hierarchical merger algorithms, while also incorporating the stopping rule, based on the objective function. The approach resolves the cluster number issue, as the solutions obtained include both the content and the number of clusters. Further, it is demonstrated how the bi-partial principle can be effectively applied to a wide variety of problems in data analysis..The book offers a valuable resource for all data scientists who wish to broaden their perspective on basic approaches and essential problems, and to thus find answers to questions that are often overlooked or h | | 出版日期 | Book 2020 | | 关键词 | Computational Intelligence; Cluster Analysis; Data Analysis; Bi-partial Objective Function; Preference A | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-13389-4 | | isbn_softcover | 978-3-030-13391-7 | | isbn_ebook | 978-3-030-13389-4Series ISSN 1860-949X Series E-ISSN 1860-9503 | | issn_series | 1860-949X | | copyright | Springer Nature Switzerland AG 2020 |
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发表于 2025-3-21 19:32:06
