Abstract
Clustering algorithm analysis, including time and space complexity analysis, has always been discussed in the literature. The emergence of big data has also created a lot of challenges for this issue. Because of high complexity and execution time, traditional clustering techniques cannot be used for such an amount of data. This problem has been addressed in this research. To present the clustering algorithm using a bee colony algorithm and high-speed read/write performance, Map-Reduce architecture is used. Using this architecture allows the proposed method to cluster any volume of data, and there is no limit to the amount of data. The presented algorithm has good performance and high precision. The simulation results on 3 datasets show that the presented algorithm is more efficient than other big data clustering methods. Also, the results of our algorithm execution time on huge datasets are much better than other big data clustering approaches.
Highlights
Nowadays, scientists believe that the issue of big data is the biggest challenge in computer science
Multiāmachine clustering techniques Sampling and reduction methods improve the performance of clustering algorithms on big datasets, But due to the growing data, these methods are no longer effective
This research offered a method for clustering large amounts of data, while in addition to maintaining the quality and desirability of data clustering, its execution time is appropriate to run on a large volume of data
Summary
Scientists believe that the issue of big data is the biggest challenge in computer science. CLARA reduces the quadratic and time complexity needed for algorithm execution to the linear function of the number of data. Data partitioning into the batches before clustering and their parallel processing reduce the computation time.
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