Sift - Local Outlier Factor Dialog

The Local Outlier Factor is a machine learning algorithm that detects outliers by using nearest neighbour distances (k-nearest neighbour). The LOF finds points that are outliers relative to local clusters. The LOF outlier score takes into account the relative density of the data points to the local clusters.

The Local Outlier Factor is found on the toolbar and under 'Outlier Detection Using PCA' in the Analysis menu.

Dialog

The Local Outlier Factor using PCA allows the user to automatically search the data and identify traces that are outside the norm. The search for outliers can be done by Combined Groups, Group, and Workspaces. Users can decide if they want to auto-exclude any outliers that are detected in the groups or workspaces. Users can specify conditions of the LOF technique, including number of nearest neighbours, thresholds, p-values, and outlier percentage.

• Grouping to Search: How PC scores are grouped together.
  • Combined Groups: Groups all scores together regardless of actual group.
  • Groups: Groups scores from the same group together.
  • Workspaces: Groups scores from the same group and workspace together (Only available when PCA is run on traces and NOT Workspace Means).
• Group passes: The number of passes to make on each group.
• Workspace passes: The number of passes to make on each workspace.
• Separate conditions in test: If conditions should be treated as separate groups.
• Auto-exclude results: If outliers should be automatically excluded from the results.
• Number of Neighbors: .
• High variability PC threshold: .
• Threshold Criteria: .
• P-Value PC Model Scores: .
• P-Value Residual Model Scores: .
• Scale Data To % Variability: .
• Run Analysis on Residuals: .
• Outlier Percentage (approx.): .
• Show densities on workspace scores: .

Results

The LOF results appear upon completion of running the test.