With the growth of big data and the increase in data volume and diversity, the complexity of data is also increasing. Traditional clustering methods provide only a single clustering result, which limits data exploration to one possible partition. In contrast, multi-class clustering methods reveal several non-repetitive and distinct clustering solutions simultaneously or sequentially. This allows for the identification of hidden data structures. Therefore, multi-class clustering has become a popular and promising area of research today. In this study, an approach and algorithm for the problem of multiclass clustering focused on the importance of objects were developed.
| Mualliflar | Ollamberganov, Fayzulla, Kalbayev , Allambergen, Yelmuratov , Qudiyar, Qudaynazarov , Muxammed |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2025-03-23 |
| Son | 1 |
| Betlar | 60-66 |
| Til | Rus |
Кластеризaция, мультиклассовая кластеризaция, кластеризaция без учителя, метод MAXIMUS., Clustering, multi-class clustering, teacherless clustering, MAXIMUS method., Klasterlash, multisinfli klasterlash, o‘qituvchisiz klasterlash, MAXIMUS usuli.
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