Interpretable clustering: an optimization approach
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DOI: 10.1007/s10994-020-05896-2
Abstract: State-of-the-art clustering algorithms provide little insight into the rationale for cluster membership, limiting their interpretability. In complex real-world applications, the latter poses a barrier to machine learning adoption when experts are asked to provide detailed… read more here.
Keywords: optimization approach; interpretable clustering; optimization; clustering optimization ... See more keywords
A Comprehensive and Reproducible Comparison of Clustering and Optimization Rules in Wi-Fi Fingerprinting
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DOI: 10.1109/tmc.2020.3017176
Abstract: Wi-Fi fingerprinting is a well-known technique used for indoor positioning. It relies on a pattern recognition method that compares the captured operational fingerprint with a set of previously collected reference samples (radio map) using a… read more here.
Keywords: clustering optimization; reproducible comparison; comprehensive reproducible; radio map ... See more keywords
A hybrid Aquila optimizer and its K-means clustering optimization
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DOI: 10.1177/01423312221111607
Abstract: Aiming at the defects of the Aquila optimizer (AO) in dealing with some complex optimization problems, such as slow convergence speed, low convergence accuracy, and easy to fall into local optimum, in this paper, a… read more here.
Keywords: hybrid aquila; clustering optimization; aquila optimizer; convergence ... See more keywords