The C++ source code (Code::Block IDE) for outlier detection in high dimensions. It covers standard methods and its approximations to detect outliers in high-dimensional data sets, including - kNN, kNNW, Sam1NN - LOF - ABOF, approxABOF - VOA, FastVOA - L1Depth, SamDepth Parameters: <NUM_POINT> <NUM_DIM> <FILE_NAME> <METHOD> <ADDITIONAL_PARAMS> - NUM_POINT : number of points (N) - NUM_DIM : number of dimensions (D) - FILE_NAME : filename of dataset with the matrix format N x D - <METHOD> with <ADDITIONAL_PARAMS> "kNN": SIGMOD 00 - Efficient Algorithms for Mining Outliers from Large Data Sets - k (recommendation: 10) "Sam1NN": NIPS 13 - Rapid Distance-Based Outlier Detection via Sampling - number of sample points (recommendation: 20) "kNNW": TKDE 05 - Outlier Mining in Large High-Dimensional Data Sets - k (recommendation: 10) "LOF": SIGMOD 00 - LOF: Identifying Density-Based Local Outliers - minPts (recommendation: 40) "ABOF": KDD 08 - Angle-Based Outlier Detection in High-dimensional Data "approxABOF": KDD 08 - Angle-Based Outlier Detection in High-dimensional Data - k (recommendation: 0.1 * N) "VOA": KDD 12 - A near-linear time approximation algorithm for angle-based outlier detection in high-dimensional data "FastVOA": KDD 12 - A near-linear time approximation algorithm for angle-based outlier detection in high-dimensional data - number of random projections (recommendation: 100) - AMS Sketch size S1 (recommendation: 3200) - AMS Sketch size S2 (recommendation: 5) "L1D": PKDD 18 - L1-Depth Revisited - A Robust Angle-based Outlier Factor in High-dimensional Space "BasicSamL1D": PKDD 18 - L1-Depth Revisited - A Robust Angle-based Outlier Factor in High-dimensional Space - number of sample pairs (recommendation: sqrt(N)) "SamDepth": PKDD 18 - L1-Depth Revisited - A Robust Angle-based Outlier Factor in High-dimensional Space - number of sample points (recommendation: sqrt(N)) Example: - 60839 41 "C:\_Data\Dataset\_L1D\Datasets\KDDCup99_norm_idf_60839_41_246.txt" "Sam1NN" 20 - 60839 41 "C:\_Data\Dataset\_L1D\Datasets\KDDCup99_norm_idf_60839_41_246.txt" "FastVOA" 100 3200 5
greatwallisme/Outlier
The C++ source code (Code::Block IDE) for outlier detection in high dimensions. It covers standard methods and its approximations to detect outliers in high-dimensional data sets, including - kNN, kNNW, Sam1NN - LOF - ABOF, approxABOF - VOA, FastVOA - L1Depth, SamDepth
C++MIT