Spatio-temporal research frequently results in analyzing large sets of data (i.e., a data set larger than will reside in common PC main memory). Currently, many analytical techniques used to analyze large data sets begin by sampling the data such that it can all reside in main memory. Depending upon the research question posed, information can be lost when outliers are discarded. For example, if the focus of the analysis is on clusters of automobiles, the outliers may not be represented in the sampled dataset. The purpose of this study is to use similarity measures to detect anomalies. The clustering algorithm that is used in this thesis research is DBSCAN. Synthetic data is generated and then analyzed to evaluate the effectiveness of detecting anomalies using similarity measures. Results from this study support the hypothesis, "If similarity measures can be developed, then DBSCAN can be used to find anomalies in trajectory data using time slices." Synthetic data is analyzed using DBSCAN to address the research question -"Can DBSCAN be used to find anomalies in trajectory data using time slices?"
|Commitee:||Crk, Igor, McKenney, Mark|
|School:||Southern Illinois University at Edwardsville|
|School Location:||United States -- Illinois|
|Source:||MAI 53/03M(E), Masters Abstracts International|
|Subjects:||Information science, Computer science|
|Keywords:||Clustering, Data analysis, Movement data, Spatio-temporal data, Time slices, Trajectories|
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