Abstract
Clustering has been proven to produce better results when applied to learning problems that fall under semi-supervised paradigms, where only incomplete or partial information about the dataset is available to perform clustering. Classic constrained clustering and recent monotonic clustering problems belong to the semi-supervised learning paradigm, although a combination of both has never been addressed. This study aims to prove that the fusion of the background knowledge leveraged by the two aforementioned semi-supervised clustering techniques results in improved performance. To do so, a hybrid objective function combining them is proposed and optimized by means of an expectation minimization scheme. The capabilities of the proposed method are tested in a wide variety of datasets with incremental levels of background knowledge and compared to purely monotonic clustering and purely constrained clustering methods belonging to the state-of-the-art. Bayesian statistical testing is used to validate the obtained results.
Our work has been supported by the research projects PID2020-119478GB-I00, A-TIC-434-UGR20 and PREDOC_01648.
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References
Benavoli, A., Corani, G., Demšar, J., Zaffalon, M.: Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis. J. Mach. Learn. Res. 18(1), 2653–2688 (2017)
Bradley, P.S., Bennett, K.P., Demiriz, A.: Constrained K-means clustering. Microsoft Research, Redmond, vol. 20 (2000)
Cano, J.R., Gutiérrez, P.A., Krawczyk, B., Woźniak, M., García, S.: Monotonic classification: an overview on algorithms, performance measures and data sets. Neurocomputing 341, 168–182 (2019)
Carrasco, J., García, S., del Mar Rueda, M., Herrera, F.: rNPBST: an R Package covering non-parametric and Bayesian statistical tests. In: Martínez de Pisón, F.J., Urraca, R., Quintián, H., Corchado, E. (eds.) HAIS 2017. LNCS (LNAI), vol. 10334, pp. 281–292. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59650-1_24
Carrasco, J., García, S., Rueda, M., Das, S., Herrera, F.: Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: practical guidelines and a critical review. Swarm Evol. Comput. 54, 100665 (2020)
Davidson, I., Basu, S.: A survey of clustering with instance level constraints. ACM Trans. Knowl. Discov. Data 1, 1–41 (2007)
González, S., Herrera, F., García, S.: Monotonic random forest with an ensemble pruning mechanism based on the degree of monotonicity. N. Gener. Comput. 33(4), 367–388 (2015)
González-Almagro, G., Luengo, J., Cano, J.R., García, S.: DILS: constrained clustering through dual iterative local search. Comput. Oper. Res. 121, 104979 (2020)
Hubert, L., Arabie, P.: Comparing partitions. J. classif. 2(1), 193–218 (1985)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. (CSUR) 31(3), 264–323 (1999)
Law, M.H.C., Topchy, A., Jain, A.K.: Clustering with soft and group constraints. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds.) SSPR /SPR 2004. LNCS, vol. 3138, pp. 662–670. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-27868-9_72
Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129–137 (1982)
Rosenfeld, J., De Smet, Y., Debeir, O., Decaestecker, C.: Assessing partially ordered clustering in a multicriteria comparative context. Pattern Recogn. 114, 107850 (2021)
Schmidt, J., Brandle, E.M., Kramer, S.: Clustering with attribute-level constraints. In: 2011 IEEE 11th International Conference on Data Mining, pp. 1206–1211. IEEE (2011)
Triguero, I., et al.: KEEL 3.0: an open source software for multi-stage analysis in data mining. Int. J. Comput. Intell. Syst. 10(1), 1238–1249 (2017)
Vouros, A., Vasilaki, E.: A semi-supervised sparse K-means algorithm. Pattern Recogn. Lett. 142, 65–71 (2021)
Wagstaff, K., Cardie, C., Rogers, S., Schrödl, S.: Constrained K-means clustering with background knowledge. In: Proceedings of the Eighteenth International Conference on Machine Learning, pp. 577–584. Morgan Kaufmann Publishers Inc. (2001)
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González-Almagro, G., Bermejo, P.S., Suarez, J.L., Cano, JR., García, S. (2022). Monotonic Constrained Clustering: A First Approach. In: Fujita, H., Fournier-Viger, P., Ali, M., Wang, Y. (eds) Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence. IEA/AIE 2022. Lecture Notes in Computer Science(), vol 13343. Springer, Cham. https://doi.org/10.1007/978-3-031-08530-7_61
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