Towards an Evaluation of Air Surveillance Track Clustering Algorithms via External Cluster Quality Measures.

摘要

Clustering is a data mining technique for analysing large data sets and finding groups of elements within the data set that are similar to each other. The use of clustering on archives of historical air surveillance track data would enable the discovery of flights that exhibited similar behaviour and followed similar flight paths. However there are many different clustering algorithms available, so some method for selecting the best from the competing algorithms is required. Unfortunately the academic literature has yet to provide a general, comprehensive, and robust methodology for this task. Further the niche nature of the problem domain means the academic literature provides no direct assistance by way of reporting practical experience in the use of particular algorithms on air surveillance track data. This report aims to fill the gap by describing such a methodology for evaluating and choosing between competing clustering algorithms.