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  • Why Are There So Many Clustering Algorithms, and How Valid Are Their Results?

    Author(s)
    Estivill-Castro, V
    Griffith University Author(s)
    Estivill-Castro, Vladimir
    Year published
    2015
    Metadata
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    Abstract
    Validity is a fundamental aspect of any machine learning approach. All the three types of current validity approaches (external, internal, and relative) have serious drawbacks and are computationally expensive. This chapter discusses why there are so many proposals for clustering algorithms and why they detach from approaches to validity. It presents a new approach that differs radically from the three families of validity approaches. The approach consists of translating the clustering validity problems to an assessment of the easiness of learning in the resulting supervised learning instances. The chapter shows that this ...
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    Validity is a fundamental aspect of any machine learning approach. All the three types of current validity approaches (external, internal, and relative) have serious drawbacks and are computationally expensive. This chapter discusses why there are so many proposals for clustering algorithms and why they detach from approaches to validity. It presents a new approach that differs radically from the three families of validity approaches. The approach consists of translating the clustering validity problems to an assessment of the easiness of learning in the resulting supervised learning instances. The chapter shows that this idea meets formal principles of cluster quality measures, and thus the intuition inspiring approach has a solid theoretical foundation. In fact, it relates to the notion of reproducibility. Finally, the chapter demonstrates that the principle applies to crisp clustering algorithms and fuzzy clustering methods.
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    Book Title
    Reproducibility: Principles, Problems, Practices, and Prospects
    DOI
    https://doi.org/10.1002/9781118865064.ch8
    Subject
    Pattern Recognition and Data Mining
    Publication URI
    http://hdl.handle.net/10072/143470
    Collection
    • Book chapters

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