Research Article
Identification of V-Formations and Circular and Doughnut Formations in a Set of Moving Entities with Outliers
Listing 1
Outlier detection algorithm.
ALGORITHM: Outlier detection on a characteristic line | INPUT: lineMembers = //Array of entities | //Threshold for Pearson’s coefficient | percentageOutliers //Maximum percentage of outliers permitted on lineMembers | OUTPUT: outliers = // Array of outliers | BEGIN | (1) Obtain the equation of the line which most suits the positions () of the entities in lineMembers, see (2). | (2) Get the maximum number of outliers permitted on lineMembers: nbrOutliers = . | (3) Find the nbrOutliers entities in lineMembers which have the maximum distance to . | (4) Remove from lineMembers the entities found in Step 3. | (5) Calculate Pearson’s coefficient using positions () of each entity in lineMembers: | Pearson = PearsonCoefficient(lineMembers). | (6) If Pearson then return in outliers the entities found in Step 3. | END |
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