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

Öz

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.

Anahtar Kelimeler

• Spatio-temporal veri madenciliği,hareketli cisimler,yörünge verileri,yörünge sapması algılama,destek vektör makinesi

Sağlık Hizmetlerinde Aykırı Dataların Kestirimi İçin Mekansal Zamansal Veri Madenciliğinin Kullanımı

Öz

Son on yılda, hareketli nesnelerden yararlı bilgi çıkarma, mekansal-zamansal veri madenciliği alanında RFID ve GPS gibi cihazların kullanımının yaygınlaşması ile yaygınlaşmıştır. Bu amaçla, veri madenciliğinin bir alt alanı olan belirsizlik tespit yöntemi, diş hekimliği servisindeki hastaların ve hastalıkların gidişatına uygulanmıştır. Bu makalede, mesafe ve yoğunluk tabanlı yöntemleri birleştiren TRAOD ve TOD-SS algoritmaları tercih edilmiştir. Bu algoritmalar, diğer aykırı algılama tekniklerinden farklı olarak, hareketli nesne yörüngesini bir bütün olarak ele almaz. Her bir parçanın yörüngeleri parçalara ayırarak komşularına göre farklı davranış sergileyip sergilemediklerini araştırıyorlar. Dolayısıyla, diğer algoritmaların bulamayan yörünge parçalarını algılarlar. Bu çalışmada tercih edilen algoritmalar geliştirdiğimiz COMB-O modelinde kullanılmış ve performansları karşılaştırılmıştır. Buna ek olarak, bölgeye ve klinikte, hastaların sınıflandırması yapılmıştır. Ayrıca, mekansal-zamansal veri madenciliğinin bir başka dalı olan kümeleme, yörünge için gerçekleştirildi. COMB-O modeli yürütüldüğünde, sonuçlar yörünge verilerinden sapmış olan alt yörüngeleri yörünge aykırı değer algılama algoritmaları yardımıyla başarıyla tespit edildiğini gösterdi. Algılanan tutarsız yörüngeler önemli veriler sağlamıştır. Buna ek olarak, DVM'nin doğrusal olmayan sınıflandırma özelliklerinden faydalanarak başarılı bir sınıflandırma gerçekleştirildi. Ayrıca, kümeleme algoritmaları olan CB-SMoT ve DB-SMoT kullanılarak Diş Hekimliği Fakültesindeki durmalar ve hareketler tespit edildi.

Anahtar Kelimeler

• Spatio-temporal data mining,moving objects,trajectory data,trajectory outlier detection,support vector machine

Kaynakça

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