Weighted adjacent matrix for K-means clustering
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Date
2019-12
Open Access Location
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Science+Business Media, LLC
Rights
Abstract
K-means clustering is one of the most popular clustering algorithms and has been embedded in other clustering algorithms, e.g. the last step of spectral clustering. In this paper, we propose two techniques to improve previous k-means clustering algorithm by designing two different adjacent matrices. Extensive experiments on public UCI datasets showed the clustering results of our proposed algorithms significantly outperform three classical clustering algorithms in terms of different evaluation metrics.
Description
CAUL read and publish agreement 2022
Keywords
k-means clustering, Similarity measurement, Adjacent matrix, Unsupervised learning
Citation
MULTIMEDIA TOOLS AND APPLICATIONS, 2019, 78 (23), pp. 33415 - 33434