Abstract
One of the most essential challenges in Data Mining and Knowledge Discovery is the development of effective tools able to find regularities in data. In order to highlight and to extract interesting knowledge from the data at hand, a key problem is frequent pattern mining, i.e. to discover frequent substructures hidden in the available data. In many interesting application fields, data are often represented and stored as sequences over time or space of generic objects. Due to the presence of noise and uncertainties in data, searching for frequent subsequences must employ approximate matching techniques, such as edit distances. A common procedure to identify recurrent patterns in noisy data is based on clustering algorithms relying on some edit distance between subsequences. However, this plain approach can produce many spurious patterns due to multiple pattern matchings on close positions in the same sequence excerpt. In this paper, we present a method to overcome this drawback by applying an optimization-based step lter that identifies the most descriptive patterns among those found by the clustering process, and allows to return more compact and easily interpretable clusters. We evaluate the mining systems performances on synthetic data in two separate cases, corresponding respectively to two different (simulated) sources of noise. In both cases, our method performs well in retrieving the original patterns with acceptable information loss.
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Notes
- 1.
The subscript “e” stands for “extraction” as in extraction step.
References
Possemato, F., Rizzi, A.: Automatic text categorization by a granular computing approach: facing unbalanced data sets. In: The 2013 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2013)
Modugno, V., Possemato, F., Rizzi, A.: Combining piecewise linear regression and a granular computing framework for financial time series classification (2014)
Bianchi, F., Livi, L., Rizzi, A., Sadeghian, A.: A granular computing approach to the design of optimized graph classification systems. Soft Comput. 18, 393–412 (2014)
Bianchi, F.M., Scardapane, S., Livi, L., Uncini, A., Rizzi, A.: An interpretable graph-based image classifier. In: 2014 International Joint Conference on Neural Networks (IJCNN), pp. 2339–2346. IEEE (2014)
Rizzi, A., Del Vescovo, G.: Automatic image classification by a granular computing approach. In: Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, pp. 33–38 (2006)
Del Vescovo, G., Rizzi, A.: Automatic classification of graphs by symbolic histograms. In: IEEE International Conference on Granular Computing. GRC 2007, pp. 410–410 (2007)
Del Vescovo, G., Rizzi, A.: Online handwriting recognition by the symbolic histograms approach. In: IEEE International Conference on Granular Computing. GRC 2007, pp. 686–686 (2007)
Bargiela, A., Pedrycz, W.: Granular Computing: An Introduction. Springer (2003)
Livi, L., Rizzi, A., Sadeghian, A.: Granular modeling and computing approaches for intelligent analysis of non-geometric data. Appl. Soft Comput. 27, 567–574 (2015)
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the Eleventh International Conference on Data Engineering, pp. 3–14. IEEE (1995)
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: ACM SIGMOD Record, vol. 22, pp. 207–216. ACM (1993)
Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Press, New York (2011)
Srikant, R., Agrawal, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. Springer (1996)
Zaki, M.J.: Spade: an efficient algorithm for mining frequent sequences. Mach. Learn. 42, 31–60 (2001)
Han, J., Pei, J., Mortazavi-Asl, B., Chen, Q., Dayal, U., Hsu, M.C.: Freespan: frequent pattern-projected sequential pattern mining. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 355–359 (2000)
Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.C.: Prefixspan: mining sequential patterns efficiently by prefix-projected pattern growth. In: 2013 IEEE 29th International Conference on Data Engineering (ICDE), IEEE Computer Society, pp. 0215–0215 (2001)
Sinha, S., Tompa, M.: YMF: a program for discovery of novel transcription factor binding sites by statistical overrepresentation. Nucleic Acids Res. 31, 3586–3588 (2003)
Pavesi, G., Mereghetti, P., Mauri, G., Pesole, G.: Weeder web: discovery of transcription factor binding sites in a set of sequences from co-regulated genes. Nucleic Acids Res. 32, W199–W203 (2004)
Eskin, E., Pevzner, P.A.: Finding composite regulatory patterns in dna sequences. Bioinformatics 18, S354–S363 (2002)
Buhler, J., Tompa, M.: Finding motifs using random projections. J. Comput. Biol. 9, 225–242 (2002)
Zhu, F., Yan, X., Han, J., Yu, P.S.: Efficient discovery of frequent approximate sequential patterns. In: Seventh IEEE International Conference on Data Mining. ICDM 2007, pp. 751–756. IEEE (2007)
Ji, X., Bailey, J.: An efficient technique for mining approximately frequent substring patterns. In: Seventh IEEE International Conference on Data Mining Workshops. ICDM Workshops 2007, pp. 325–330. IEEE (2007)
Rizzi, A., Possemato, F., Livi, L., Sebastiani, A., Giuliani, A., Mascioli, F.M.F.: A dissimilarity-based classifier for generalized sequences by a granular computing approach. In: IJCNN, IEEE, pp. 1–8 (2013)
Zhu, F., Yan, X., Han, J., Yu, P.S.: Efficient discovery of frequent approximate sequential patterns. In: Proceedings of the 2007 Seventh IEEE International Conference on Data Mining, Washington, DC, USA, IEEE Computer Society, pp. 751–756 (2007)
Ji, X., Bailey, J.: An efficient technique for mining approximately frequent substring patterns. In: Proceedings of the Seventh IEEE International Conference on Data Mining Workshops. ICDMW ’07, Washington, DC, USA, IEEE Computer Society, pp. 325–330 (2007)
Fu, A.W.C., Keogh, E., Lau, L.Y., Ratanamahatana, C.A., Wong, R.C.W.: Scaling and time warping in time series querying. VLDB J. Int. J. Very Large Data Bases 17, 899–921 (2008)
Vlachos, M., Kollios, G., Gunopulos, D.: Discovering similar multidimensional trajectories. In: 18th International Conference on Data Engineering. Proceedings. IEEE, pp. 673–684 (2002)
Patel, P., Keogh, E., Lin, J., Lonardi, S.: Mining motifs in massive time series databases. In: 2002 IEEE International Conference on Data Mining. ICDM 2003. Proceedings. IEEE, pp. 370–377 (2002)
Chiu, B., Keogh, E., Lonardi, S.: Probabilistic discovery of time series motifs. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 493–498 (2003)
Floratou, A., Tata, S., Patel, J.M.: Efficient and accurate discovery of patterns in sequence data sets. IEEE Trans. Knowl. Data Eng. 23, 1154–1168 (2011)
Matsui, T., Uno, T., Umemori, J., Koide, T.: A new approach to string pattern mining with approximate match. In: Discovery Science, pp. 110–125. Springer (2013)
Maiorino, E., Possemato, F., Modugno, V., Rizzi, A.: Information granules filtering for inexact sequential pattern mining by evolutionary computation (2014)
Rizzi, A., Del Vescovo, G., Livi, L., Frattale Mascioli, F.M.: A new granular computing approach for sequences representation and classification. In: Proceedings of the 2012 International Joint Conference on Neural Networks, pp. 2268–2275 (2012)
Del Vescovo, G., Livi, L., Frattale Mascioli, M., Rizzi, A.: On the problem of modeling structured data with the minsod representative. Int. J. Comput. Theory Eng. 6, 9–14 (2014)
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Maiorino, E., Possemato, F., Modugno, V., Rizzi, A. (2016). Noise Sensitivity of an Information Granules Filtering Procedure by Genetic Optimization for Inexact Sequential Pattern Mining. In: Merelo, J.J., Rosa, A., Cadenas, J.M., Dourado, A., Madani, K., Filipe, J. (eds) Computational Intelligence. IJCCI 2014. Studies in Computational Intelligence, vol 620. Springer, Cham. https://doi.org/10.1007/978-3-319-26393-9_9
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