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Biblioqrafik təsvir | Alguliyev , R.M. Improved parallel Big data clustering based on k-medoids and k-means algorithms / R.M. Alguliyev , R.M. Aliguliyev , L.V. Sukhostat // Problems of Information Technology. - 2024. - N: vol. 15, no. 1.- P. 18-25. | Annotasiya | In recent years, the amount of data created worldwide has grown exponentially. The
increase in computational complexity when working with "Big data" leads to the need to
develop new approaches for their clustering. The problem of massive data amounts
clustering can be solved using parallel processing. Dividing the data into batches helps to
perform clustering in a reasonable time. In this case, the reliability of the obtained result for
each block will affect the performance of the entire dataset. The main idea of the proposed
approach is to apply the k-medoids and k-means algorithms to parallel Big data clustering.
The advantage of this hybrid approach is that it is based on the central object in the cluster
and is less sensitive to outliers than k-means clustering. Experiments are conducted on real
datasets, namely YearPredictionMSD and Phone Accelerometer. The proposed approach is
compared with the k-means and MiniBatch k-means algorithms. Experimental results
proved that the proposed parallel implementation of k-medoids with the k-means
algorithm shows greater accuracy and works faster than the k-means algorithm | Elektron variant | Elektron variant |
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