Robust Categorical Data Clustering Guided by Multi-Granular Competitive Learning

Data set composed of categorical features is very common in big data analysis tasks. Since categorical features are usually with a limited number of qualitative possible values, the nested granular cluster effect is prevalent in the implicit discrete distance space of categorical data. That is, data...

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Vydané v:	Proceedings of the International Conference on Distributed Computing Systems s. 288 - 299
Hlavní autori:	Cai, Shenghong, Zhang, Yiqun, Luo, Xiaopeng, Cheung, Yiu-Ming, Jia, Hong, Liu, Peng
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 23.07.2024
Predmet:	Big Data Boosting categorical feature Cluster analysis cluster granularity Clustering algorithms Competitive learning Distributed databases Encoding Euclidean distance number of clusters
ISSN:	2575-8411
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