Towards Understanding Clustering Problems and Algorithms: An Instance Space Analysis
نویسندگان
چکیده
Various criteria and algorithms can be used for clustering, leading to very distinct outcomes potential biases towards datasets with certain structures. More generally, the selection of most effective algorithm applied a given dataset, based on its characteristics, is problem that has been largely studied in field meta-learning. Recent advances form new methodology known as Instance Space Analysis provide an opportunity extend such meta-analyses gain greater visual insights relationship between datasets’ characteristics performance different algorithms. The aim this study perform first time clustering problems As result, we are able analyze impact choice test instances employed, strengths weaknesses some popular algorithms,
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ژورنال
عنوان ژورنال: Algorithms
سال: 2021
ISSN: ['1999-4893']
DOI: https://doi.org/10.3390/a14030095