The Use of Spatio-Temporal Data Mining for Detection and Interpretation of Trajectory Outliers in Health Care Services

: In the last decade, useful information extraction from moving objects has become widespread in the

spatial-temporal data mining field with the increasing use of devices such as RFID and GPS. For this purpose,

the outlier detection method, which is a subfield of data mining, was applied to the trajectory of patients and

diseases in the dental health service. In this article, TRAOD and TOD-SS algorithms combining distance and

density-based methods were preferred. These algorithms do not handle the moving object trajectory as a whole

unlike other outlier detection techniques. They investigate whether each piece exhibits different behavior

according to its neighbors by separating trajectories into pieces. So, they detect outlying trajectory pieces that

other algorithms cannot locate. Algorithms preferred in this study were used in a COMB-O model we

developed and their performances were compared. In addition, according to the region and clinic, the

classification of patients was made. Also, clustering, which is another branch of spatial-temporal data mining,

was performed for trajectory. When the COMB-O model was executed, results showed sub-trajectories that

deviated from the trajectory data were successfully detected with the help of the trajectory outlier detection

algorithms. Inconsistent trajectories perceived provided significant data. In addition to this, successful

classification was performed by making use of non-linear classification features of DVM. Moreover, stops and

moves in the Faculty of Dentistry were detected by using CB-SMoT and DB-SMoT which are clustering

algorithms.