An Adaptive Clustering Algorithm Based on Local-Density Peaks for Imbalanced Data Without Parameters

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,...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering Vol. 35; no. 4; pp. 3419 - 3432
Main Authors: Tong, Wuning, Wang, Yuping, Liu, Delong
Format: Journal Article
Language:English
Published: New York IEEE 01.04.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive algorithms
Algorithms
Clustering
Clustering algorithms
Clustering methods
Computer science
Data clustering
Datasets
Density distribution
density peaks
imbalanced data
Machine learning
Machine learning algorithms
multiple centers
Parameters
Shape
Task analysis
ISSN:1041-4347, 1558-2191
Online Access:Get full text
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Summary: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.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2021.3138962