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

Year 2017, , 411 - 428, 30.09.2017

Abdulsamet Haşıloğlu , Seyma Yücel Altay Umit Ertaş

https://doi.org/10.31202/ecjse.315044

Abstract

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.

Keywords

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

Abstract

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.

Keywords

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

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