A k-Deviation Density Based Clustering Algorithm

Due to the adoption of global parameters, DBSCAN fails to identify clusters with different and varied densities. To solve the problem, this paper extends DBSCAN by exploiting a new density definition and proposes a novel algorithm called k-deviation density based DBSCAN (kDDBSCAN). Various datasets...

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Vydané v:	Mathematical problems in engineering Ročník 2018; číslo 2018; s. 1 - 16
Hlavní autori:	Jun, Li, Dongyong, Yang, Jinyin, Chen, Jungan, Chen
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cairo, Egypt Hindawi Publishing Corporation 01.01.2018 Hindawi John Wiley & Sons, Inc
Predmet:	Algorithms Artificial intelligence Bioinformatics Clustering Clusters Computer science Datasets Density Deviation Engineering International conferences Neighborhoods Noise Parameter identification Probability distribution Similarity measures
ISSN:	1024-123X, 1563-5147
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