Zhou JLiu TZhu J2019-122019-12MULTIMEDIA TOOLS AND APPLICATIONS, 2019, 78 (23), pp. 33415 - 334341380-7501https://hdl.handle.net/10179/17429CAUL read and publish agreement 2022K-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.33415 - 33434k-means clusteringSimilarity measurementAdjacent matrixUnsupervised learningJournal article10.1007/s11042-019-08009-x4254291573-7721Massey_Dark0803 Computer Software0805 Distributed Computing0806 Information Systems0801 Artificial Intelligence and Image Processing