Journal
JOURNAL OF ALGORITHMS & COMPUTATIONAL TECHNOLOGY
Volume 12, Issue 1, Pages 53-61Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/1748301817735665
Keywords
DBSCAN; minPts; Eps; genetic algorithm; MapReduce
Funding
- Natural Science Foundation of Jilin Province [20150101054JC]
- Postdoctoral Research Fund of Jilin Province [40301919]
- Key Program for Science and Technology Development of Jilin Province [20150204036GX]
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This paper proposes an improved adaptive density-based spatial clustering of applications with noise (DBSCAN) algorithm based on genetic algorithm and MapReduce parallel computing programming framework to improve the poor clustering effect and low efficiency of the DBSCAN algorithm, which due to experiential solving parameters. The size of Intensive Interval Threshold minPts and Scan Radius Eps would be rational planned by genetic algorithm iterative optimization, and it is secondary statute processing with the similarity and variability of the dataset and the efficient computing power of Hadoop Cluster. The data could be reasonable serialization, and the efficient adaptive parallel clustering could be achieved ultimately. Through the experimental results, it is shown that the proposed algorithm in this paper has higher clustering accuracy and execution efficiency than that of the comparison baselines. The trend will continue to grow with the increased volume of dataset. The improved algorithm provides a more accurate implementation method for the threshold of DBSCAN algorithm, and realizes the specific calculation process, which provides practice support for the realization of DBSCAN.
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