Discriminant Chronicle Mining
Sequential pattern mining attempts to extract frequent behaviors from a sequential dataset. When sequences are labeled, it is interesting to extract behaviors that characterize each sequence class. This task is called discriminant pattern mining. In this paper, we introduce discriminant chronicle mining. Conceptually, a chronicle is a temporal graph whose vertices are events and whose edges represent numerical temporal constraints between these events. We propose DCM, an algorithm that mines discriminant chronicles. It is based on rule learning methods that extract the temporal constraints. Computational performances and discriminant power of extracted chronicles are evaluated on synthetic and real data. Finally, we apply this algorithm to the case study consisting in analyzing care pathways of epileptic patients.
This project has been founded by the French Agency of Medicines and Health Products Safety (ANSM). We would like to thank Pr. E. Oger and Pharm.D E. Polard for agreeing to study the patterns extracted from the real dataset.
- Agrawal, R. & Srikant, R. (1995). Mining sequential patterns. In Proceedings of the International Conference on Data Engineering, pp. 3–14. IEEE.Google Scholar
- Batal, I., Valizadegan, H., Cooper, G. F., & Hauskrecht, M. (2013). A temporal pattern mining approach for classifying electronic health record data. ACM Transactions on Intelligent Systems and Technology (TIST), 4(4), 63.Google Scholar
- Berlingerio, M., Bonchi, F., Giannotti, F., & Turini, F. (2007). Mining clinical data with a temporal dimension: A case study. In Proceedings of the International Conference on Bioinformatics and Biomedicine, pp. 429–436.Google Scholar
- Bornemann, L., Lecerf, J., & Papapetrou, P. (2016). STIFE: A framework for feature-based classification of sequences of temporal intervals. In International Conference on Discovery Science, pp. 85–100. Springer, Cham.Google Scholar
- Bringmann, B., Nijssen, S., & Zimmermann, A. (2011). Pattern-based classification: a unifying perspective. arXiv preprint arXiv:1111.6191.
- Concaro, S., Sacchi, L., Cerra, C., Fratino, P., & Bellazzi, R. (2009). Mining healthcare data with temporal association rules: Improvements and assessment for a practical use. In Conference on Artificial Intelligence in Medicine in Europe, pp. 16–25.Google Scholar
- Dauxais, Y., Guyet, T., Gross-Amblard, D., & Happe, A. (2017). Discriminant chronicles mining: Application to care pathways analytics. In Proceedings of the Conference on Artificial Intelligence in Medicine, pp. 234–244. Springer, Cham.Google Scholar
- Dong, G., & Li, J. (1999). Efficient mining of emerging patterns: Discovering trends and differences. In Proceedings of ACM SIGKDD, pp. 43–52.Google Scholar
- Doran, G., & Ray, S. (2014). A theoretical and empirical analysis of support vector machine methods for multiple-instance classification. Machine Learning, 97(1), 79–102.Google Scholar
- Dousson, C., & Duong, T. V. (1999). Discovering chronicles with numerical time constraints from alarm logs for monitoring dynamic systems. In Proceedings of International Conference on Artificial Intelligence, pp. 620–626.Google Scholar
- Fabrègue, M., Braud, A., Bringay, S., Le Ber, F., & Teisseire, M. (2013). Orderspan: Mining closed partially ordered patterns. In International Symposium on Intelligent Data Analysis, pp. 186–197. Springer, Heidelberg.Google Scholar
- Guyet, T., & Quiniou, R. (2011). Extracting temporal patterns from interval-based sequences. In Proceedings of International Joint Conference on Artificial Intelligence, pp. 1306–1311.Google Scholar
- Lakshmanan, G. T., Rozsnyai, S., & Wang, F. (2013). Investigating clinical care pathways correlated with outcomes. In Business process management, pp. 323–338. Springer, Heidelberg.Google Scholar
- Lattner, A. D., Kim, S., Cervone, G., & Grefenstette, J. J. (2003). Experimental comparison of symbolic learning programs for the classification of gene network topology models. Center for Computing Technologies-TZI, 2, 1.Google Scholar
- Lipton, Z. C. (2016). The mythos of model interpretability. arXiv preprint arXiv:1606.03490.
- Mäntyjärvi, J., Himberg, J., Kangas, P., Tuomela, U., & Huuskonen, P. (2004). Sensor signal data set for exploring context recognition of mobile devices. In Proceedings of 2nd International Conference on Pervasive Computing (PERVASIVE 2004), pp. 18–23.Google Scholar
- Papapetrou, P., Kollios, G., Sclaroff, S., & Gunopulos, D. (2005). Discovering frequent arrangements of temporal intervals. In Fifth IEEE International Conference on Data Mining, pp. 8–pp. IEEE.Google Scholar
- Pei, J., Han, J., & Wang, W. (2002). Mining sequential patterns with constraints in large databases. In Proceedings of the International Conference on Information and Knowledge Management, pp. 18–25. ACM.Google Scholar
- Quiniou, R., Cordier, M., Carrault, G., & Wang, F. (2001). Application of ILP to cardiac arrhythmia characterization for chronicle recognition. In Proceedings of International Conference on Inductive Logic Programming, pp. 220–227.Google Scholar
- Sahuguède, A., Fergani, S., Le Corronc, E., & Le Lann, M.-V. (2018). Mapping chronicles to a k-dimensional Euclidean space via random projections. In 14th International Conference on Automation Science and Engineering (CASE), 6p. IEEE.Google Scholar
- Santisteban, J. & Tejada-Cárcamo, J. (2015). Unilateral Jaccard similarity coefficient. In GSB@ SIGIR, pp. 23–27.Google Scholar
- Uno, T., Kiyomi, M., & Arimura, H. (2004). LCM ver. 2: Efficient mining algorithms for frequent/closed/maximal itemsets. In FIMI, vol. 126.Google Scholar