Unsupervised learning in latent space with a fuzzy logic guided modified BA

Kiełkowicz, Kazimierz

doi:10.4467/2353737XCT.18.121.8896

Abstract

In this paper, a modified bat algorithm with fuzzy inference mamdani-type system is applied to the problem of document clustering in a semantic features space induced by SVD decomposition. The algorithm learns the optimal clustering of the documents as well as the optimal number of clusters in a concept space; thus, making it suitable for a large and spare dataset which occur in information retrieval system. a centroid-based solution in multidimensional space is evaluated with a silhouette index. A TF-IDF method is used to represent documents in vector space. The presented algorithm is tested on the 20 newsgroup dataset.

References

[1] Baziar A., Kavoosi-Fard A.A., Zare J., A Novel Self Adoptive Modification Approach Based on Bat Algorithm for Optimal Management of Renewable MG, Journal of Intelligent Learning System and Application, Vol. 5, Issue 1, 2013, 11–18.10.4236/jilsa.2013.51002
Search in Google Scholar Back to article
[2] Caliński R., Harabasz J., A dendrite method for cluster analysis, Commun. Stat., Vol. 3, No. 1, 1–27, 197.
Search in Google Scholar Back to article
[3] Davies D., Bouldin D., A cluster separation measure, IEEE Trans. Pattern Anal. Mach. Intell., Vol. PAMI–1, No. 2, 224–227.10.1109/TPAMI.1979.4766909
Search in Google Scholar Back to article
[4] Dorigo M., Maziezzo V., Colorni A., The ant system: optimization by a colony of cooperating ants, IEEE Trans. on Systems, Man and Cybernetics B, Vol. 26, No. 1, 1996, 29–41.10.1109/3477.48443618263004
Search in Google Scholar Back to article
[5] Dorigo M., Stützle T., Ant Colony Optimization, MIT Press, 200410.7551/mitpress/1290.001.0001
Search in Google Scholar Back to article
[6] Eberhart R. C., Shi Y., Empirical Study of Particle Swarm Optimization, 1999
Search in Google Scholar Back to article
[7] Eberhart R., Shi Y., Particle swarm optimization: Developments, applications, and recourses, Proc. Congr. EVol. Comput., 2001, 81–86.
Search in Google Scholar Back to article
[8] Fister I. Jr, Fister D., Yang X.S., A Hybrid Bat Algorithm, Elektrotehniski Vestnik, 2013, 1–7.10.1155/2014/709738400067225187904
Search in Google Scholar Back to article
[9] Fong S., Yang X.S., Karamanglu M., Bat Algorithm for Topology Optimization in Microelectronic Application, International Conference on Future Generation Communication Technology (FGCT), IEEE, 2012, 150–155.10.1109/FGCT.2012.6476566
Search in Google Scholar Back to article
[10] Goldberg D.E., Genetic Algorithms in Search, Optimization and Machine Learning, Kluwer Academic Publishers, 1989.
Search in Google Scholar Back to article
[11] Hansen P., Jaumard B., Cluster analysis and mathematical programming, Math. Program., Vol. 79, 1997, 191–215.10.1007/BF02614317
Search in Google Scholar Back to article
[12] Hofmann T., Probabilistic Latent Semantic Analysis, Proc of the Fifteenth conference on Uncertainty in artificial intelligence, UAI’99, 289–296.
Search in Google Scholar Back to article
[13] Kennedy J., Eberhart R.C., Particle swarm optimization, Proc. of IEEE International Conference on Neural Networks, Vol. 4, 1995, 1942–1948.
Search in Google Scholar Back to article
[14] Kennedy J., Eberhart R., Shi Y., Swarm Intelligence, Academic, 2001.
Search in Google Scholar Back to article
[15] Kiełkowicz K., Grela D., FLC control for tuning exploration phase in bio–inspired metaheuristic, Annales Universitatis Mariae Curie–Sklodowska, sectio AI – Informatica, 2017.10.17951/ai.2016.16.2.32
Search in Google Scholar Back to article
[16] Kiełkowicz K., Grela D., Modified Bat Algorithm for Nonlinear Optimization, International Journal of Computer Science and Network Security (IJCSNS), 2016, 46–50.
Search in Google Scholar Back to article
[17] Melin P., Olivas F., Castillo O., Valdez F., Soria J., Valdez M., Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic, Expert Systems with Applications, Vol. 40, Issue 8, 2013, 3196–3206.10.1016/j.eswa.2012.12.033
Search in Google Scholar Back to article
[18] Merwe D., Engelbrecht A., Data clustering using particle swarm optimization, Proc. Congre. EVol. Comput, Vol. 1, 215–220.
Search in Google Scholar Back to article
[19] Mirjalili S., Mirjalili S. M., Yang Xin–She, Binary Bat Algorithm, Neural Computing and Applications, Vol. 25, Issue 3, 2014, 663–681.10.1007/s00521-013-1525-5
Search in Google Scholar Back to article
[20] Storn R., Price K., Differential Evolution – A Simple and Efficient Adaptive Scheme for Global Optimization Over Continuous Spaces, Technical Report, 1995.
Search in Google Scholar Back to article
[21] Xu R., Xu J., Wunsch C., A Comparison Study of Validity Indices on Swarm–Intelligence–Based Clustering, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, PART B: CYBERNETICS, Vol. 42, No. 4, 201210.1109/TSMCB.2012.2188509
Search in Google Scholar Back to article
[22] Yang X. S., A New Metaheuristic Bat–Inspired Algorithm, Nature Inspired Cooperative Strategies for Optimization, 2010, 65–74.10.1007/978-3-642-12538-6_6
Search in Google Scholar Back to article
[23] Yang X. S., Bat Algorithm for Multi-Objective Optimization, International Journal of Bio-Inspired Computation, Vol. 3, Issue 5, 2011, 267–27410.1504/IJBIC.2011.042259
Search in Google Scholar Back to article
[24] Rousseeuw P., Silhouettes: Silhouettes: a Graphical Aid to the Interpretation and Validation of Cluster Analysis, Computational and Applied Mathematics 20, 53–65.10.1016/0377-0427(87)90125-7
Search in Google Scholar Back to article
[25] Sparck Jones, K., A statistical interpretation of term specificity and its application in retrieval, Journal of Documentation, 28, 11–21.10.1108/eb026526
Search in Google Scholar Back to article
[26] Sparck Jones, K., IDF term weighting and IR research lessons, Journal of Documentation, 60(6), 521–523.10.1108/00220410410560591
Search in Google Scholar Back to article

Unsupervised learning in latent space with a fuzzy logic guided modified BA

Abstract

Paradigm

My account