Abstract
We propose a clustering method based on the calculation of variables derived from the \(\alpha \)-dependent Rényi information entropy – a point information gain entropy (\(H_\alpha \)) and point information gain entropy density (\(\varXi _\alpha \)), which measure an information-entropic distance between two multidimensional distributions. The matrices of \(H_\alpha \)/\(\varXi _\alpha \) values as functions of the parameter \(\alpha \) and a label of a multidimensional set’s object are classified into groups using a standard k-means algorithm. The method is presented on two multi-image series which in the origin, the number of images in the sets, the number of image color channels, and the pixel resolution differ.
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Acknowledgments
This work was financially supported by CENAKVA (No. CZ.1.05/2.1.00/01.0024), CENAKVA II (No.LO1205 under the NPU I program) and The CENAKVA Centre Development (No. CZ.1.05/2.1.00/19.0380).
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Rychtáriková, R. (2016). Clustering of Multi-image Sets Using Rényi Information Entropy. In: Ortuño, F., Rojas, I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2016. Lecture Notes in Computer Science(), vol 9656. Springer, Cham. https://doi.org/10.1007/978-3-319-31744-1_46
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DOI: https://doi.org/10.1007/978-3-319-31744-1_46
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