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Experimental Code for KDD 2017 For paper: Scalable Top-N Local Outlier Detection If you find any problems, please contact yyan2@wpi.edu.

Includes three parts:

  1. Baseline Methods

    Two different indexing methods for KNN search are implemented.

    --- Pivot-based KNN Search :

     Main Class: baseline.pivotknn.ComputeTopNLOF

 From Paper: Bhaduri, Kanishka, Bryan L. Matthews, and Chris R. Giannella. "Algorithms for speeding up distance-based outlier detection." Proceedings of the 17th ACM SIGKDD international conference on Knowledge Discovery and Data Mining. ACM, 2011.

    --- R-Tree based KNN Search:

     Main Class: baseline.rtreeknn.ComputeTopNLOF

 From Paper: Guttman, Antonin. R-trees: a dynamic index structure for spatial searching. Vol. 14. No. 2. ACM, 1984.

  2. MicroCluster --- State-of-the-art method:

     Main Class: microcluster.topnlof.TopNLOFDetection

 From Paper: Jin, Wen, Anthony KH Tung, and Jiawei Han. "Mining top-n local outliers in large databases." Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2001.

  3. TOLF --- Proposed method

     Two versions of TOLF: Single-thread and Multi-thread version. 

 Main Class for Single-Thread Version: cellpruning.lof.pruning.ComputeTopNLOFWithPruning

 Main Class for Multi-Thread Version: cellpruning.lof.pruning.MultiThread.ComputeTopNLOFWithPruning

 From Paper: Scalable Top-N Local Outlier Detection (In Submission)