Abstract
k-means algorithm is popularly used as an effective clustering method. However, existing k-means algorithm usually performs poorly in imbalanced datasets. To address this problem, density-kmeans++ algorithm based on density distance is proposed in this paper. The proposed method incorporates density distance into traditional Euclidean distance-based k-means algorithm when clustering imbalanced dataset. The experimental results on UCI datasets and Western Reserve University Bearing Data indicates that density-kmeans++ has better ability to deal with imbalanced datasets than k-means++.
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Fan, L., Chai, Y., Li, Y. (2020). A Density-Based k-Means++ Algorithm for Imbalanced Datasets Clustering. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 594. Springer, Singapore. https://doi.org/10.1007/978-981-32-9698-5_5
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DOI: https://doi.org/10.1007/978-981-32-9698-5_5
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