Abstract
We have developed a variational learning approach for finite Scaled Dirichlet mixture model with local model selection framework. By gradually splitting the components, our model is able to reach convergence as well as obtain the optimal number of clusters. By tackling real life challenging problems including spam detection and object clustering, the proposed model’s flexibility and performance are validated.
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References
Aggarwal, C.C.: Data Classification: Algorithms and Applications. Frontiers in Physics. Chapman and Hall/CRC, New York (2014)
Bdiri, T., Bouguila, N.: Positive vectors clustering using inverted dirichlet finite mixture models. Expert Syst. Appl. 39(2), 1869–1882 (2012)
Bouguila, N., Ziou, D.: Unsupervised selection of a finite dirichlet mixture model: an mml-based approach. IEEE Trans. Knowl. Data Eng. 18(8), 993–1009 (2006)
Bouguila, N., Elguebaly, T.: A fully bayesian model based on reversible jump mcmc and finite beta mixtures for clustering. Expert Syst. Appl. 39(5), 5946–5959 (2012)
Bouguila, N., Ziou, D.: Using unsupervised learning of a finite dirichlet mixture model to improve pattern recognition applications. Pattern Recogn. Lett. 26(12), 1916–1925 (2005)
Bouguila, N., Ziou, D., Hammoud, R.I.: On bayesian analysis of a finite generalized dirichlet mixture via a metropolis-within-gibbs sampling. Pattern Anal. Appl. 12(2), 151–166 (2009)
Bourouis, S., Bouguila, N., Li, Y., Azam, M.: Visual scene reconstruction using a bayesian learning framework. In: Mansouri, A., El Moataz, A., Nouboud, F., Mammass, D. (eds.) ICISP 2018. LNCS, vol. 10884, pp. 225–232. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94211-7_25
Constantinopoulos, C., Likas, A.: Unsupervised learning of gaussian mixtures based on variational component splitting. IEEE Trans. Neural Netw. 18(3), 745–755 (2007)
Dredze, M., Gevaryahu, R., Elias-Bachrach, A.: Learning fast classifiers for image spam, January 2007
Dua, D., Graff, C.: UCI machine learning repository (2017)
Everitt, B.S., Landau, S., Leese, M.: Cluster Analysis, 4th edn. Wiley Publishing, New York (2009)
Fan, W., Bouguila, N., Ziou, D.: Variational learning for finite dirichlet mixture models and applications. IEEE Trans. Neural Netw. Learn. Syst. 23(5), 762–774 (2012)
Fan, W., Bouguila, N., Ziou, D.: Variational learning of finite dirichlet mixture models using component splitting. Neurocomputing 129, 3–16 (2014)
Fu, S., Bouguila, N.: A Bayesian intrusion detection framework. In: 2018 International Conference on Cyber Security and Protection of Digital Services (Cyber Security), pp. 1–8, June 2018
Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset. Technical report, 7694, California Institute of Technology (2007)
Hong, J.: The state of phishing attacks. Commun. ACM 55(1), 74–81 (2012)
Ihou, K.E., Bouguila, N.: Variational-based latent generalized dirichlet allocation model in the collapsed space and applications. Neurocomputing 332, 372–395 (2019)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
McLachlan, G.J., Peel, D.: Finite Mixture Models. Wiley Series in Probability and Statistics. Wiley, New York (2000)
Mehdi, M., Bouguila, N., Bentahar, J.: Trustworthy web service selection using probabilistic models. In: 2012 IEEE 19th International Conference on Web Services, Honolulu, HI, USA, 24–29 June 2012, pp. 17–24 (2012)
Oboh, B.S., Bouguila, N.: Unsupervised learning of finite mixtures using scaled dirichlet distribution and its application to software modules categorization. In: 2017 IEEE International Conference on Industrial Technology (ICIT), pp. 1085–1090, March 2017
Parisi, G.: Statistical Field Theory. Frontiers in Physics. Addison-Wesley Pub. Co., Boston (1988)
Siponen, M., Stucke, C.: Effective anti-spam strategies in companies: an international study. In: Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS 2006), vol. 6, p. 127c, January 2006
Acknowledgement
The completion of this research was made possible thanks to the Natural Sciences and Engineering Research Council of Canada (NSERC) and Concordia University Research Chair Tier 2.
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Nguyen, H., Maanicshah, K., Azam, M., Bouguila, N. (2019). Data Clustering Using Variational Learning of Finite Scaled Dirichlet Mixture Models with Component Splitting. In: Karray, F., Campilho, A., Yu, A. (eds) Image Analysis and Recognition. ICIAR 2019. Lecture Notes in Computer Science(), vol 11663. Springer, Cham. https://doi.org/10.1007/978-3-030-27272-2_10
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DOI: https://doi.org/10.1007/978-3-030-27272-2_10
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