| DC Field | Value | Language |
|---|
| dc.contributor.author | Zhang Hengrui | - |
| dc.contributor.author | German, Yu. О. | - |
| dc.contributor.author | He Runhai | - |
| dc.coverage.spatial | Минск | en_US |
| dc.date.accessioned | 2025-01-13T06:27:15Z | - |
| dc.date.available | 2025-01-13T06:27:15Z | - |
| dc.date.issued | 2024 | - |
| dc.identifier.citation | Zhang Hengrui. Principles and applications of fuzzy clustering algorithms / Zhang Hengrui, Yu. О. German, He Runhai // Информационные технологии и системы 2024 (ИТС 2024) = Information Technologies and Systems 2024 (ITS 2024) : материалы международной научной конференции, Минск, 20 ноября 2024 г. / Белорусский государственный университет информатики и радиоэлектроники ; редкол.: Л. Ю. Шилин [и др.]. – Минск, 2024. – С. 196–197. | en_US |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/58693 | - |
| dc.description.abstract | This paper takes the fuzzy C-mean (FCM) clustering algorithm as an example, introduces the basic principles and applications of fuzzy clustering, and compares it with the K-mean algorithm to analyze the similarities and differences between the two. Fuzzy clustering allows data points to belong to multiple clusters at the same time, is suitable for dealing with fuzzy and overlapping data, and has a wide range of practical applications. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | БГУИР | en_US |
| dc.subject | материалы конференций | en_US |
| dc.subject | information technology | en_US |
| dc.subject | clustering | en_US |
| dc.subject | Fuzzy C-means | en_US |
| dc.title | Principles and applications of fuzzy clustering algorithms | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | ИТС 2024
|
