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Soft Computing

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01.06.2017 | Methodologies and Application

Locally adaptive multiple kernel k-means algorithm based on shared nearest neighbors

verfasst von: Shifei Ding, Xiao Xu, Shuyan Fan, Yu Xue

Erschienen in: Soft Computing | Ausgabe 14/2018

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Abstract

Most of multiple kernel clustering algorithms aim to find the optimal kernel combination and have to calculate kernel weights iteratively. For the kernel methods, the scale parameter of Gaussian kernel is usually searched in a number of candidate values of the parameter and the best is selected. In this paper, a novel locally adaptive multiple kernel k-means algorithm is proposed based on shared nearest neighbors. Our similarity measure meets the requirements of the clustering hypothesis, which can describe the relations between data points more reasonably by taking local and global structures into consideration. We assign to each data point a local scale parameter and combine the parameter with shared nearest neighbors to construct kernel matrix. According to the local distribution, the local scale parameter of Gaussian kernel is generated adaptively. Experiments show that the proposed algorithm can effectively deal with the clustering problem of data sets with complex structure or multiple scales.

Vorheriger Artikel On special elements and pseudocomplementation in lattices with antitone involutions

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Titel: Locally adaptive multiple kernel k-means algorithm based on shared nearest neighbors
verfasst von: Shifei Ding
Xiao Xu
Shuyan Fan
Yu Xue
Publikationsdatum: 01.06.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 14/2018
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-017-2640-5

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