Weighted Clustering for Anomaly Detection in Big Data

Rasim Alguliyev, Ramiz Aliguliyev, Yadigar Imamverdiyev, Lyudmila Sukhostat


In this paper, a new method for anomaly detection based on weighted clustering is proposed. The weights that were obtained by summing the weights of each point from the data set are assigned to clusters. The comparison is made using seven datasets (of large dimensions) with the k-means algorithm. The proposed approach increases the reliability of data partitioning into groups. Experimental results show that the proposed approach becomes more efficient with increasing size of the analysed dataset.


Clustering; weighted clustering; clustering evaluation metrics; Big data; anomaly detection; k-means.


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DOI: 10.19139/soic.v6i2.404


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