Menyu
ITI
əməkdaslarının elmi isləri
Elektron kitabxana
Konfranslar İnformasiya Sistemi
Qəzetlər
UOT 004
|
ITI əməkdaşlarının elmi işləri - məqalə |
Biblioqrafik təsvir | Alguliyev , R.M. PSO+K-means Algorithm for Anomaly Detection in Big Data / R.M. Alguliyev , R.M. Aliguliyev , F.D. Abdullayeva // Statistics, Optimization and Information Computing. - 2019. - N: June,Vol. 7.- P. 348–359pp. | Annotasiya | The use of clustering methods in anomaly detection is considered as an effective approach. The choice of the
cluster primary center and the finding of local optimum in the well-known k-means and other classic clustering algorithms
are considered as one of the major problems and do not allow to get accurate results in anomaly detection. In this paper to
improve the accuracy of anomaly detection based on the combination of PSO (particle swarm optimization) and k-means
algorithms, the new weighted clustering method is proposed. The proposed method is tested on Yahoo! S5 dataset and a
comparative analysis of the obtained results with the k-means algorithm is performed. The results of experiments show that
compared to the k-means algorithm the proposed method is more robust and allows to get more accurate results.
| Elektron variant | Elektron variant |
|
________
© ict.az http://ict.az/az
|
|