›› 2015, Vol. 30 ›› Issue (6): 1233-1248.doi: 10.1007/s11390-015-1596-0

Special Issue: Data Management and Data Mining

• Special Section on Networking and Distributed Computing for Big Data • Previous Articles     Next Articles

An Efficient Algorithm for Distributed Outlier Detection in Large Multi-Dimensional Datasets

Xi-Te Wang(王习特), Student Member, CCF, Member, ACM, De-Rong Shen(申德荣), Senior Member, CCF, Member, ACM, IEEE, Mei Bai(白梅), Tie-Zheng Nie(聂铁铮), Member, CCF, ACM, Yue Kou(寇月), Member, CCF, ACM, Ge Yu(于戈), Fellow, CCF, Member, ACM, IEEE   

  1. College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
  • Received:2015-05-15 Revised:2015-10-14 Online:2015-11-05 Published:2015-11-05
  • About author:Xi-Te Wang is a Ph.D. candidate in computer software and theory, Northeastern University, Shenyang, from which he also received his M.S. degree in computer architecture in 2011. He is a student member of CCF, and a member of ACM. His research interests include cloud computing and big data management.
  • Supported by:

    This work was supported by the National Basic Research 973 Program of China under Grant No. 2012CB316201, the National Natural Science Foundation of China under Grant Nos. 61033007 and 61472070, and the Fundamental Research Funds for the Central Universities of China under Grant No. N120816001.

The distance-based outlier is a widely used definition of outlier. A point is distinguished as an outlier on the basis of the distances to its nearest neighbors. In this paper, to solve the problem of outlier computing in distributed environments, DBOZ, a distributed algorithm for distance-based outlier detection using Z-curve hierarchical tree (ZH-tree) is proposed. First, we propose a new index, ZH-tree, to effectively manage the data in a distributed environment. ZH-tree has two desirable advantages, including clustering property to help search the neighbors of a point, and hierarchical structure to support space pruning. We also design a bottom-up approach to build ZH-tree in parallel, whose time complexity is linear to the number of dimensions and the size of dataset. Second, DBOZ is proposed to compute outliers in distributed environments. It consists of two stages. 1) To avoid calculating the exact nearest neighbors of all the points, we design a greedy method and a new ZH-tree based k-nearest neighbor searching algorithm (ZHkNN for short) to obtain a threshold LW. 2) We propose a filter-and-refine approach, which first filters out the unpromising points using LW, and then outputs the final outliers through refining the remaining points. At last, the efficiency and the effectiveness of ZH-tree and DBOZ are testified through a series of experiments.

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