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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Veröffentlicht in:	IEEE transactions on knowledge and data engineering Jg. 35; H. 4; S. 3419 - 3432
Hauptverfasser:	Tong, Wuning, Wang, Yuping, Liu, Delong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.04.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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-Zugang:	Volltext
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