Business Intelligence

Clustering Moving Objects Using Segments Slopes

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Executive Summary

Given a set of moving object trajectories, the authors show how to cluster those using k-means clustering approach. The proposed clustering algorithm is competitive with the k-means clustering because it specifies the value of "k" based on the segment's slope of the moving object trajectories. The advantage of this approach is that it overcomes the known drawbacks of the k-means algorithm, namely, the dependence on the number of clusters (k), and the dependence on the initial choice of the clusters' centroids, and it uses segment's slope as a heuristic to determine the different number of clusters for the k-means algorithm.

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