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In this chapter, we first describe data sets used in numerical experiments and group them into different subclasses according to their size. Then, we discuss the implementations of the incremental clustering algorithms described in previous chapters. We also provide some recommendations on the choice of parameters of the algorithms. In addition, the choice of parameters in the algorithm for finding initial cluster centers, which is an important part of all nonsmooth optimization-based clustering algorithms, is discussed.

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Implementations and Data Sets

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  • 发表日期：

    2025年

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  • Adil Bagirov,
  • Napsu Karmitsa,
  • Sona Taheri

摘要

In this chapter, we first describe data sets used in numerical experiments and group them into different subclasses according to their size. Then, we discuss the implementations of the incremental clustering algorithms described in previous chapters. We also provide some recommendations on the choice of parameters of the algorithms. In addition, the choice of parameters in the algorithm for finding initial cluster centers, which is an important part of all nonsmooth optimization-based clustering algorithms, is discussed.