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

Yıl 2017, Cilt: 4 Sayı: 3, 411 - 428, 30.09.2017
https://doi.org/10.31202/ecjse.315044

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.

Kaynakça

  • [1] Rao, K.V., Govardhan, A., Rao, K.V.C., “Spatiotemporal Data Mining: Issues, Tasks and Applications”, International Journal of Computer Science and Engineering Survey ( IJCSES), vol.3, issue 1, 2012, pp. 39-52.
  • [2] Geetha, R., Sumathi, N., Sathiabama, D. S., “A Survey of Spatial, Temporal and Spatio-Temporal Data Mining”, Journal of Computer Applications, vol.1 issue 4, 2008, pp. 31- 33.
  • [3] Alvares, L.O., Palma, A.T., Oliveira, G., Bogorny, V., “WEKA-STPM: From Trajectory Samples to Semantic Trajectories”, in: Proceedings of the Workshop on Open Source Code, Porto Alegre, Brazil 2010, pp. 1–6.
  • [4] Rocha, J.A., Times, V.C., Oliveira, G., Alvares, L.O., Bogorny, V., “DB-SMoT: A direction-based spatio-temporal clustering method”, IEEE Conf. of Intelligent Systems, 2010, pp. 114-119.
  • [5] Sharma, L.K., Vyas, O.P., Scheider, S., Akasapu, A., “Nearest Neighbor Classification for Trajectory Data ITC”, Springer LNCS CCIS 101, 2010, pp. 180–185.
  • [6] Almeida, V.T., and Güting, R.H., “Indexing the Trajectories of Moving Objects in Networks”, Geoinformatica, vol. 9, issue 1, 2005, pp. 33-60.
  • [7] Gogoi, P., Bhattacharyya, D., Borah, B., and Kalita, J.K., “A survey of outlier detection methods in network anomaly identification”, The Computer Journal, vol.54, issue 4, 2011, pp. 570–588.
  • [8] Kriegel, H.-P., Kroger, P., and Zimek, A., “Outlier Detection Techniques”, in: Tutorial at the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2009.
  • [9] Ng, R.T., and Han, J., “Efficient and effective clustering methods for spatial data mining”, Proceeding of 94 VLDB, 1994, pp. 144–155.
  • [10] Ester, M., Kriegel, H-P., and Xu, X., “A database interface for clustering in large spatial databases”, in: Proceedings of 1st International Conference on Knowledge Discovery and Data Mining (KDD-95), 1995.
  • [11] Zhang, T., Ramakrishnan R., and Livny, M., “BIRCH: An efficient data clustering method for very large databases”, Proceedings of the 96 ACM SIGMOD International Conference on Management of Data, New York, NY, USA 1996, pp. 103-114.
  • [12] Guha, S., Rastogi, R., and Shim, K., “CURE: An efficient clustering algorithm for large databases”. SIGMOD Rec., vol. 27 issue 2, 1998, pp. 73–84.
  • [13] Cateni, S., Colla, V., and Vannucci, M., “Outlier Detection Methods for Industrial Application”, in: J. Aramburo, A.R. Trevino (Eds.), Advances in Robotics, Automation and Control, Austria, 2008, pp. 265-281.
  • [14] Ben-Gal. I., “Outlier Detection”, in: O. Maimon, L. Rokach (Eds.), Data Mining and Knowledge Discovery Handbook, Kluwer Academic Publisher, 2005.
  • [15] Mansur, M.O., and Noor Md. Sap, M., “Outlier Detection Technique in Data Mining: A Research Perspective”, in: Proceedings of the Postgraduate Annual Research Seminar, 2005.
  • [16] Knorr, E.M., and Ng, R., “Algorithms for Mining Distance-Based Outliers in Large Datasets”, Proceedings of VLDB, 1988, pp 392-403.
  • [17] Knorr, E.M., Ng, R.T., and Tueakov, V., “Distance-Based Outliers: Algorithms and Applications”, in: Proc. VLDB Journal, vol.8 issue 3, 2000, pp. 237-253.
  • [18] Ramaswamy, S., Rastogi R., and Shim, K., “Efficient algorithms for mining outliers from large data sets”, Proceedings of the International Conference on Management of Data, Dallas, Texas, USA, 2000.
  • [19] Breunig, M. M., Kriegel, H-P., Ng, R.T., and Sander, J., “LOF: Identifying Density-Based Local Outliers”, Proceedings of the 2000 ACMSIGMOD international conference on management of data, Dallas, Texas, United States, ACM Press New York, NY, USA, 2000, pp. 93–104.
  • [20] Papadimitriou, S., Kitawaga, H., Gibbons, P., and Faloutsos, V., “LOCI: Fast Outlier detection using the local correlation integral”, Proceedings of the International Conference on Data Engineering, 2003, pp. 315-326.
  • [21] Birant, D., and Kut, A., “Spatio-temporal Outlier Detection in Large Databases”, in: 28th International Conference on Information Technology Interfaces, 2006, pp. 179–184.
  • [22] Wu, E., Liu, W., and Chawla, S., “Spatio-Temporal Outlier Detection In Precipitation Data”, Proceedings of the Second international conference on Knowledge Discovery from Sensor Data, Las Vegas, NV,2008, pp. 115-133.
  • [23] Bogorny, V., “Spatial and Spatio-Temporal Data Mining”, Trajectory Knowledge Discovery, IEEE ICDM, Universidade Federal de Santa Catarina, 2010.
  • [24] Spaccapietra, S., Parent, C., Damiani, M.L., Macedo, J.A.F., Porto, F., and Vangenot, C., “A conceptual view on trajectories”, Data Knowl. Eng., vol. 65, issue 1, 2008, pp. 126-146.
  • [25] Lee, J., Han, J., and Li, X., “Trajectory Outlier Detection: A Partition-and-Detect Framework”, 24. Int'l Conf. on Data Engineering, 2008, pp. 140-149.
  • [26] Yuan, G., Xia, S., Zhang, L., Zhou, Y., and Ji, C., “Trajectory Outlier Detection Algorithm Based on Structural Features”, Journal of Computational Information Systems, vol.7, issue 11, 2011, pp. 4137–4144.
  • [27] Hsu, C.-W., Chang, C.-C., and Lin, C.-J., “A practical guide to support vector classification”, Tech. rep., Department of Computer Science, National Taiwan University, 2003.
  • [28] Weston, J., “Support Vector Machine”, In: Tutorial, 4 Independence Way, Princeton, USA
  • [29] Spaccapietra, S., Parent, C., Damiani, M.L., Macedo, A.D., Porto, F.,Vangenot, C., “A conceptual view on trajectories”, Data and Knowledge Engineering, vol 65, issue 1, 2008, pp. 126-146.
  • [30] Alvares, L.O., Bogorny, V., Palma, A., Kuijpers, B., Moelans, B., Macedo, J.A.F., “Towards Semantic Trajectory Knowledge Discovery”, Technical Report, Hasselt University, Belgium 2007.
  • [31] Palma A.T., and Bogorny, V., “A Clustering-Based Approach for Discovering Interesting Places in Trajectories, Master of Computer Science Thesis”, Unıversıdade Federal Do Rıo Grande Do Sul Instıtuto De Informátıca Programa De Pós-Graduação Em Computação, Porto Alegre, 2008.
  • [32] Palma, A.T., Bogorny, V., Kuijpers, B., and Alvares, L.O., “A clustering-based approach for discovering interesting places in trajectories”, in: ACMSAC, New York, NY, USA, ACM Press, 2008, pp. 863– 868.
  • [33] Rocha, J.A.M.R., Times, V.C., Oliveria, G., Alvares, L.O., Bogorny, V., “DB-SMoT: A direction-based spatio-temporal clustering method”, IEEE Conf. of Intelligent Systems, 2010, pp. 114-119.

Sağlık Hizmetlerinde Aykırı Dataların Kestirimi İçin Mekansal Zamansal Veri Madenciliğinin Kullanımı

Yıl 2017, Cilt: 4 Sayı: 3, 411 - 428, 30.09.2017
https://doi.org/10.31202/ecjse.315044

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.

Kaynakça

  • [1] Rao, K.V., Govardhan, A., Rao, K.V.C., “Spatiotemporal Data Mining: Issues, Tasks and Applications”, International Journal of Computer Science and Engineering Survey ( IJCSES), vol.3, issue 1, 2012, pp. 39-52.
  • [2] Geetha, R., Sumathi, N., Sathiabama, D. S., “A Survey of Spatial, Temporal and Spatio-Temporal Data Mining”, Journal of Computer Applications, vol.1 issue 4, 2008, pp. 31- 33.
  • [3] Alvares, L.O., Palma, A.T., Oliveira, G., Bogorny, V., “WEKA-STPM: From Trajectory Samples to Semantic Trajectories”, in: Proceedings of the Workshop on Open Source Code, Porto Alegre, Brazil 2010, pp. 1–6.
  • [4] Rocha, J.A., Times, V.C., Oliveira, G., Alvares, L.O., Bogorny, V., “DB-SMoT: A direction-based spatio-temporal clustering method”, IEEE Conf. of Intelligent Systems, 2010, pp. 114-119.
  • [5] Sharma, L.K., Vyas, O.P., Scheider, S., Akasapu, A., “Nearest Neighbor Classification for Trajectory Data ITC”, Springer LNCS CCIS 101, 2010, pp. 180–185.
  • [6] Almeida, V.T., and Güting, R.H., “Indexing the Trajectories of Moving Objects in Networks”, Geoinformatica, vol. 9, issue 1, 2005, pp. 33-60.
  • [7] Gogoi, P., Bhattacharyya, D., Borah, B., and Kalita, J.K., “A survey of outlier detection methods in network anomaly identification”, The Computer Journal, vol.54, issue 4, 2011, pp. 570–588.
  • [8] Kriegel, H.-P., Kroger, P., and Zimek, A., “Outlier Detection Techniques”, in: Tutorial at the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2009.
  • [9] Ng, R.T., and Han, J., “Efficient and effective clustering methods for spatial data mining”, Proceeding of 94 VLDB, 1994, pp. 144–155.
  • [10] Ester, M., Kriegel, H-P., and Xu, X., “A database interface for clustering in large spatial databases”, in: Proceedings of 1st International Conference on Knowledge Discovery and Data Mining (KDD-95), 1995.
  • [11] Zhang, T., Ramakrishnan R., and Livny, M., “BIRCH: An efficient data clustering method for very large databases”, Proceedings of the 96 ACM SIGMOD International Conference on Management of Data, New York, NY, USA 1996, pp. 103-114.
  • [12] Guha, S., Rastogi, R., and Shim, K., “CURE: An efficient clustering algorithm for large databases”. SIGMOD Rec., vol. 27 issue 2, 1998, pp. 73–84.
  • [13] Cateni, S., Colla, V., and Vannucci, M., “Outlier Detection Methods for Industrial Application”, in: J. Aramburo, A.R. Trevino (Eds.), Advances in Robotics, Automation and Control, Austria, 2008, pp. 265-281.
  • [14] Ben-Gal. I., “Outlier Detection”, in: O. Maimon, L. Rokach (Eds.), Data Mining and Knowledge Discovery Handbook, Kluwer Academic Publisher, 2005.
  • [15] Mansur, M.O., and Noor Md. Sap, M., “Outlier Detection Technique in Data Mining: A Research Perspective”, in: Proceedings of the Postgraduate Annual Research Seminar, 2005.
  • [16] Knorr, E.M., and Ng, R., “Algorithms for Mining Distance-Based Outliers in Large Datasets”, Proceedings of VLDB, 1988, pp 392-403.
  • [17] Knorr, E.M., Ng, R.T., and Tueakov, V., “Distance-Based Outliers: Algorithms and Applications”, in: Proc. VLDB Journal, vol.8 issue 3, 2000, pp. 237-253.
  • [18] Ramaswamy, S., Rastogi R., and Shim, K., “Efficient algorithms for mining outliers from large data sets”, Proceedings of the International Conference on Management of Data, Dallas, Texas, USA, 2000.
  • [19] Breunig, M. M., Kriegel, H-P., Ng, R.T., and Sander, J., “LOF: Identifying Density-Based Local Outliers”, Proceedings of the 2000 ACMSIGMOD international conference on management of data, Dallas, Texas, United States, ACM Press New York, NY, USA, 2000, pp. 93–104.
  • [20] Papadimitriou, S., Kitawaga, H., Gibbons, P., and Faloutsos, V., “LOCI: Fast Outlier detection using the local correlation integral”, Proceedings of the International Conference on Data Engineering, 2003, pp. 315-326.
  • [21] Birant, D., and Kut, A., “Spatio-temporal Outlier Detection in Large Databases”, in: 28th International Conference on Information Technology Interfaces, 2006, pp. 179–184.
  • [22] Wu, E., Liu, W., and Chawla, S., “Spatio-Temporal Outlier Detection In Precipitation Data”, Proceedings of the Second international conference on Knowledge Discovery from Sensor Data, Las Vegas, NV,2008, pp. 115-133.
  • [23] Bogorny, V., “Spatial and Spatio-Temporal Data Mining”, Trajectory Knowledge Discovery, IEEE ICDM, Universidade Federal de Santa Catarina, 2010.
  • [24] Spaccapietra, S., Parent, C., Damiani, M.L., Macedo, J.A.F., Porto, F., and Vangenot, C., “A conceptual view on trajectories”, Data Knowl. Eng., vol. 65, issue 1, 2008, pp. 126-146.
  • [25] Lee, J., Han, J., and Li, X., “Trajectory Outlier Detection: A Partition-and-Detect Framework”, 24. Int'l Conf. on Data Engineering, 2008, pp. 140-149.
  • [26] Yuan, G., Xia, S., Zhang, L., Zhou, Y., and Ji, C., “Trajectory Outlier Detection Algorithm Based on Structural Features”, Journal of Computational Information Systems, vol.7, issue 11, 2011, pp. 4137–4144.
  • [27] Hsu, C.-W., Chang, C.-C., and Lin, C.-J., “A practical guide to support vector classification”, Tech. rep., Department of Computer Science, National Taiwan University, 2003.
  • [28] Weston, J., “Support Vector Machine”, In: Tutorial, 4 Independence Way, Princeton, USA
  • [29] Spaccapietra, S., Parent, C., Damiani, M.L., Macedo, A.D., Porto, F.,Vangenot, C., “A conceptual view on trajectories”, Data and Knowledge Engineering, vol 65, issue 1, 2008, pp. 126-146.
  • [30] Alvares, L.O., Bogorny, V., Palma, A., Kuijpers, B., Moelans, B., Macedo, J.A.F., “Towards Semantic Trajectory Knowledge Discovery”, Technical Report, Hasselt University, Belgium 2007.
  • [31] Palma A.T., and Bogorny, V., “A Clustering-Based Approach for Discovering Interesting Places in Trajectories, Master of Computer Science Thesis”, Unıversıdade Federal Do Rıo Grande Do Sul Instıtuto De Informátıca Programa De Pós-Graduação Em Computação, Porto Alegre, 2008.
  • [32] Palma, A.T., Bogorny, V., Kuijpers, B., and Alvares, L.O., “A clustering-based approach for discovering interesting places in trajectories”, in: ACMSAC, New York, NY, USA, ACM Press, 2008, pp. 863– 868.
  • [33] Rocha, J.A.M.R., Times, V.C., Oliveria, G., Alvares, L.O., Bogorny, V., “DB-SMoT: A direction-based spatio-temporal clustering method”, IEEE Conf. of Intelligent Systems, 2010, pp. 114-119.
Toplam 33 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Abdulsamet Haşıloğlu

Seyma Yücel Altay Bu kişi benim

Umit Ertaş Bu kişi benim

Yayımlanma Tarihi 30 Eylül 2017
Gönderilme Tarihi 19 Mayıs 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 4 Sayı: 3

Kaynak Göster

IEEE A. Haşıloğlu, S. Yücel Altay, ve U. Ertaş, “Sağlık Hizmetlerinde Aykırı Dataların Kestirimi İçin Mekansal Zamansal Veri Madenciliğinin Kullanımı”, ECJSE, c. 4, sy. 3, ss. 411–428, 2017, doi: 10.31202/ecjse.315044.