A parallel approach using the parallel, advanced, slicing and pmcod algorithms with the grid and metric partitioning for distance based outlier detection on streams.
Parameters: --input --treeInput (Optional) --window --slide --dataset <dataset name for grid partitioning (works on stock, tao, fc, gauss)> --k --range --algorithm <parallel,advanced,advanced_vp,slicing,pmcod> --part (Optional) <Partitioning type - grid,metric> --VPcount (Optional) --parallelism
A parallel approach using the pamcod, sop, pmcksky and psod algorithms with the metric partitioning for multi-parameter (application parameters) distance based outlier detection on streams.
Parameters: --input --treeInput (Optional) --window --slide --dataset <dataset name for grid partitioning (works on stock, tao, fc, gauss)> --algorithm <pamcod,ksky,pmcksky,psod> --part (Optional) <Partitioning type - only metric> --VPcount (Optional) --parallelism --q <Queries of k and R separated by ";" and "," (i.e. 50;0.45,40;0.35)>
A parallel approach using the sop, pmcksky and psod algorithms with metric partitioning for multi-parameter (application and windowing parameters) distance based outlier detection on streams.
Parameters: --input --treeInput (Optional) --window <window size values separated with comma ","> --slide <slide size values separated with comma ","> --r <range values separated with comma ","> --k <number of neighbors values separated with comma ","> --dataset <dataset name for grid partitioning (works on stock, tao, fc, gauss)> --algorithm <pamcod,ksky,pmcksky,psod> --part (Optional) <Partitioning type - only metric> --VPcount (Optional) --parallelism