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An Adaptive Clustering Algorithm Based on Local-Density Peaks for Imbalanced Data Without Parameters
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DOI：10.1109/TKDE.2021.3138962.png)
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Abstract
En 中文
Imbalanced data clustering is a challenging problem in machine learning. The main difficulty is caused by the imbalance in both cluster size and data density distribution. To address this problem, we propose a novel clustering algorithm called LDPI based on local-density peaks in this study. First, an initial sub-cluster construction scheme is designed based on a 3-dimensional (3-D) decision graph that can easily detect the initial sub-cluster centers and identify the noise points. Second, a sub-cluster updating strategy is designed, which can automatically identify the false sub-cluster centers and update the initial sub-clusters. Third, a sub-cluster merging scheme is designed, which merges the updated initial sub-clusters into final clusters. Consequently, the proposed algorithm has three advantages: 1) It does not require any input parameters; 2) It can automatically determine the cluster centers and number of clusters; 3) It is suitable for imbalanced datasets and datasets with arbitrary shapes and distributions. The effectiveness of LDPI is demonstrated experimentally and the superiority of LDPI is identified by comparison with 5 state-of-the-art algorithms.
Keywords:
Clustering algorithms
Machine learning algorithms
Machine learning
Computer science
Clustering methods
Task analysis
Shape
Data clustering
density peaks
imbalanced data
multiple centers
Journal
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10.4
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3.2W