A Distributed Outlier Detection Algorithm Based on Density Clustering
LIU Yamei
YAN Renwu
Abstract：Local outlier detection algorithm is an important research direction in data mining,with the explosive growth of da?ta mining,outlier work becomes more meaningful. The current detection algorithms have many disadvantages in dealing with large-scale data. This paper combines the traditional outlier detection algorithm LOF and the MapReduce distributed framework of Hadoop distributed platform,and implements the parallelization strategy,and improves it by density clustering algorithm DBSCAN. Compared with other LOF algorithms and other improved algorithms,the proposed algorithm improves both efficiency and accuracy. Moreover,with the increase of the number of data nodes in the Hadoop system,the efficiency of the algorithm is improved accord?ingly. The experimental results show that the algorithm is feasible in dealing with large-scale data.
Keywords：local outlier detectiondensity clusteringHadoopMapReduceparallelizationlocal outlier factor
Publication Date：2019-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 1320-1325 )