Abstract
Predictive clustering is a general framework that unifies clustering and prediction. This paper investigates how to apply this framework to cluster time series data. The resulting system, Clus-TS, constructs predictive clustering trees (PCTs) that partition a given set of time series into homogeneous clusters. In addition, PCTs provide a symbolic description of the clusters. We evaluate Clus-TS on time series data from microarray experiments. Each data set records the change over time in the expression level of yeast genes as a response to a change in environmental conditions. Our evaluation shows that Clus-TS is able to cluster genes with similar responses, and to predict the time series based on the description of a gene. Clus-TS is part of a larger project where the goal is to investigate how global models can be combined with inductive databases.
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References
Blockeel, H., De Raedt, L., Ramon, J.: Top-down induction of clustering trees. In: 15th Int’l Conf. on Machine Learning, pp. 55–63 (1998)
Curk, T., Zupan, B., Petrovič, U., Shaulsky, G.: Računalniško odkrivanje mehanizmov uravnavanja istražanja genov. In: Prvo srečanje slovenskih bioinformatikov, pp. 56–58 (2005)
De Raedt, L.: A perspective on inductive databases. SIGKDD Explorations 4(2), 69–77 (2002)
Ernst, J., Nau, G.J., Bar-Joseph, Z.: Clustering short time series gene expression data. Bioinformatics 21(Suppl. 1), 159–168 (2005)
Ashburner, M., et al.: Gene Ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nature Genet. 25(1), 25–29 (2000)
Ferri, C., Flach, P.A., Hernández-Orallo, J.: Learning decision trees using the area under the ROC curve. In: 19th Int’l Conf. on Machine Learning, pp. 139–146 (2002)
Fromont, E., Blockeel, H., Struyf, J.: Integrating decision tree learning into inductive databases. In: KDID 2006. LNCS, vol. 4747, pp. 81–96. Springer, Heidelberg (2007)
Garofalakis, M., Hyun, D., Rastogi, R., Shim, K.: Building decision trees with constraints. Data Mining and Knowledge Discovery 7(2), 187–214 (2003)
Gasch, A., Spellman, P., Kao, C., Carmel-Harel, O., Eisen, M., Storz, G., Botstein, D., Brown, P.: Genomic expression program in the response of yeast cells to environmental changes. Mol. Biol. Cell. 11, 4241–4257 (2000)
Imielinski, T., Mannila, H.: A database perspective on knowledge discovery. Communications of the ACM 39(11), 58–64 (1996)
Kaufman, L., Rousseeuw, P.J. (eds.): Finding groups in data: An introduction to cluster analysis. Wiley, Chichester (1990)
Lee, S.D., De Raedt, L.: An efficient algorithm for mining string data-bases under constraints. In: Goethals, B., Siebes, A. (eds.) KDID 2004. LNCS, vol. 3377, pp. 108–129. Springer, Heidelberg (2005)
Liao, T.W.: Clustering of time series data – a survey. Pattern Recognition 38, 1857–1874 (2005)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2007)
Michalski, R.S., Stepp, R.E.: Learning from observation: conceptual clustering. In: Machine Learning: an Artificial Intelligence Approach, vol. 1, Tioga Publishing Company (1983)
Mitasiunaité, I., Boulicaut, J.-F.: Looking for monotonicity properties of a similarity constraint on sequences. In: ACM Symposium of Applied Computing SAC’2006, Special Track on Data Mining, pp. 546–552. ACM Press, New York (2006)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann series in Machine Learning. Morgan Kaufmann, San Francisco (1993)
Raileanu, L.E., Stoffel, K.: Theoretical comparison between the Gini index and information gain criteria. Annals of Mathematics and Artificial Intelligence 41(1), 77–93 (2004)
Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spokenword recognition. In: IEEE Transaction on Acoustics, Speech, and Signal Processing. LNAI, vol. ASSP-26, pp. 43–49. IEEE Computer Society Press, Los Alamitos (1978)
Sese, J., Kurokawa, Y., Monden, M., Kato, K., Morishita, S.: Constrained clusters of gene expression profiles with pathological features. Bioinformatics 20, 3137–3145 (2004)
Slavkov, I., Džeroski, S., Struyf, J., Loskovska, S.: Constrained clustering of gene expression profiles. In: Conf. on Data Mining and Data Warehouses (SiKDD 2005) at the 7th Int’l Multi-Conference on Information Society 2005, pp. 212–215 (2005)
Struyf, J., Džeroski, S.: Constraint based induction of multi-objective regression trees. In: Bonchi, F., Boulicaut, J.-F. (eds.) KDID 2005. LNCS, vol. 3933, pp. 222–233. Springer, Heidelberg (2006)
Todorovski, L., Cestnik, B., Kline, M., Lavrač, N., Džeroski, S.: Qualitative clustering of short time-series: A case study of firms reputation data. In: ECML/PKDD 2002 Workshop on Integration and Collaboration Aspects of Data Mining, Decision Support and Meta-Learning, pp. 141–149 (2002)
Torgo, L.: A comparative study of reliable error estimators for pruning regression trees. In: Coelho, H. (ed.) IBERAMIA 1998. LNCS (LNAI), vol. 1484, Springer, Heidelberg (1998)
Ženko, B., Džeroski, S., Struyf, J.: Learning predictive clustering rules. In: Bonchi, F., Boulicaut, J.-F. (eds.) KDID 2005. LNCS, vol. 3933, pp. 234–250. Springer, Heidelberg (2006)
Wagstaff, K.L.: Value, cost, and sharing: Open issues in constrained clustering. In: KDID 2006. LNCS, vol. 4747, pp. 24–41. Springer, Heidelberg (2007)
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Džeroski, S., Gjorgjioski, V., Slavkov, I., Struyf, J. (2007). Analysis of Time Series Data with Predictive Clustering Trees. In: Džeroski, S., Struyf, J. (eds) Knowledge Discovery in Inductive Databases. KDID 2006. Lecture Notes in Computer Science, vol 4747. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75549-4_5
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DOI: https://doi.org/10.1007/978-3-540-75549-4_5
Publisher Name: Springer, Berlin, Heidelberg
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