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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Vydané v:	IEEE transactions on knowledge and data engineering Ročník 35; číslo 4; s. 3419 - 3432
Hlavní autori:	Tong, Wuning, Wang, Yuping, Liu, Delong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.04.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
On-line prístup:	Získať plný text
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