Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/11788
ชื่อเรื่อง: An incremental density-based clustering framework using fuzzy local clustering
ผู้แต่ง: Laohakiat S.
Sa-ing V.
Keywords: Data streams
Large dataset
Computation time
Density-based Clustering
Large datasets
Local cluster
Local clustering
State-of-the-art algorithms
Stream data clustering
Valley seeking
Clustering algorithms
วันที่เผยแพร่: 2021
บทคัดย่อ: This paper presents a novel incremental density-based clustering framework using the one-pass scheme, named Fuzzy Incremental Density-based Clustering (FIDC). Employing one-pass clustering in which each data point is processed once and discarded, FIDC can process large datasets with less computation time and memory, compared to its density-based clustering counterparts. Fuzzy local clustering is employed in local clusters assignment process to reduce clustering inconsistencies from one-pass clustering. To improve the clustering performance and simplify the parameter choosing process, the modified valley seeking algorithm is used to adaptively determine the outlier thresholds for generating the final clusters. FIDC can operate in both traditional and stream data clustering. The experimental results show that FIDC outperforms state-of-the-art algorithms in both clustering modes. © 2020 Elsevier Inc.
URI: https://ir.swu.ac.th/jspui/handle/123456789/11788
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090056358&doi=10.1016%2fj.ins.2020.08.052&partnerID=40&md5=962fb4debe8d483bc4c7fb9e7198e4e1
ISSN: 200255
Appears in Collections:Scopus 1983-2021

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