Abstract
The control of Growing Self Organizing Maps (GSOM) algorithms presents a serious issue with huge data learning. In conjunction with the growing threshold (GT), the spread factor (SF) is used as a controlling measure of the map size during the growing process. The effect of the spread factor in fuzzy learning with Fuzzy Multilevel Interior GSOMs (FMIG) algorithm is investigated. Further analysis is conducted on very large data in order to demonstrate the spread control of data distribution with FMIG learning in comparison with Multilevel Interior Growing SOM (MIGSOM), GSOM, Fuzzy Kohonen Clustering Network (FKCN) and fuzzy GSOM. Therefore, the aim of this paper is to study the effect of the spread factor values on the map structure in term of quantization error, topology preservation and dead units. Experimental studies with huge synthetic and real datasets are fulfilled at different spread factor values for the advertised algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Tlili, M., Ayadi, T., Hamdani, T.M., Alimi, A.M.: Performance evaluation of FMIG clustering using fuzzy validity indexes. Soft Comput. 19(12), 3515–3528 (2015)
Tlili, M., Hamdani, T.M., Alimi, A.M.: Big data clustering validity. In: SoCPaR, Tunisia, pp. 348–352 (2014)
Tlili, M., Hamdani, T.M., Alimi, A.M.: FMIG: fuzzy multilevel interior growing self-organizing maps. In: IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Athens (2012)
Ayadi, T., Hamdani, T.M., Alimi, A.M.: A new data topology matching technique with multilevel interior growing self-organizing maps. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 2479–2486 (2010)
Ayadi, T., Hamdani, T.M., Alimi, A.M.: MIGSOM: multilevel interior growing self-organizing maps for high dimensional data clustering. Neural Process. Lett. 36, 235–256 (2012)
Ayadi, T., Hamdani, T.M., Alimi, A.M., Khabou, M.A.: 2IBGSOM: interior and irregular boundaries growing self-organizing maps. In: IEEE sixth International Conference on Machine Learning and Applications, pp. 387–392 (2007)
Ayadi, T., Hamdani, T.M., Alimi, A.M.: On the use of cluster validity for evaluation of MIGSOM clustering. In: ISCIII: 5th International Symposium on Computational Intelligence and Intelligent Informatics, pp. 121–126 (2011)
Alimi, A.M.: Beta neuro-fuzzy systems. TASK Q. J. 7(1), 23–41 (2003). Special Issue on Neural Networks edited by W. Duch and D. Rutkowska
Alimi, A.M., Hassine, R., Selmi, M.: Beta fuzzy logic systems: approximation properties in the MIMO case. Int. J. Appl. Math. Comput. Sci. 13(2), 225–238 (2003)
Hamdani, T.M., Alimi, A.M., Khabou, M.A.: An iterative method for deciding SVM and single layer neural network structures. Neural Process. Lett. 33(2), 171–186 (2011)
Hamdani, T.M., Alimi, A.M., Karray, F.: Enhancing the structure and parameters of the centers for BBF fuzzy neural network classifier construction based on data structure. In: Proceedings of IEEE International Join Conference on Neural Networks, IJCNN, Hong Kong, pp. 3174–3180 (2008). Art. no. 4634247
El Malek, J., Alimi, A.M., Tourki, R.: Problems in pattern classification in high dimensional spaces: behavior of a class of combined neuro-fuzzy classifiers. Fuzzy Sets Syst. 128(1), 15–33 (2002)
Pascual, A., Barcena, M., Merelo, J.J., Carazo, J.M.: Mapping and fuzzy classification of macromolecular images using self-organizing neural networks. Ultramicroscopy 84, 85–99 (2000)
Kohonen, T.: Self-organized formation of topologically correct feature maps. Biol. Cybern. 43, 59–69 (1982)
Kohonen, T.: The self-organizing map. Neurocomputing 21, 1–6 (1998)
Kohonen, T.: Self-organizing Maps. Springer, Berlin (2001)
Alahakoon, L.D., Halgamuge, S.K., Sirinivasan, B.: Dynamic self organizing maps with controlled growth for knowledge discovery. IEEE Trans. Neural Netw. 11(3), 601–614 (2000). Special Issue on Knowledge Discovery and Data Mining
Fritzke, B.: Growing cell structure a self organizing network for supervised and un-supervised learning. Neural Netw. 7, 1441–1460 (1994)
Amarasiri, R., Alahakoon, D., Smith, K.A.: HDGSOM: a modified growing self-organizing map for high dimensional data clustering. In: Fourth International Conference on Hybrid Intelligent Systems (HIS 2004), pp. 216–221 (2004)
Hsu, A.L., Halgarmuge, S.K.: Enhanced topology preservation of dynamic self-organising maps for data visualization. In: IFSA World Congress and 20th NAFIPS International Conference, vol. 3, pp. 1786–1791 (2001)
Blake, C.L., Merz, C.J.: UCI repository of machine learning databases. University of California, Department of Information and Computer Science, Irvine, CA (1998). http://www.ics.uci.edu/mlearn/mlrepository.html
Theodoridis, Y.: Spatial datasets U an unofficial collection (1996). http://www.dias.cti.gr/ytheod/research/datasets/spatial.htm
Acknowledgments
The authors would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Tlili, M., Hamdani, T.M., Alimi, A.M. (2017). Spread Control for Huge Data Fuzzy Learning. In: Abraham, A., Haqiq, A., Alimi, A., Mezzour, G., Rokbani, N., Muda, A. (eds) Proceedings of the 16th International Conference on Hybrid Intelligent Systems (HIS 2016). HIS 2016. Advances in Intelligent Systems and Computing, vol 552. Springer, Cham. https://doi.org/10.1007/978-3-319-52941-7_58
Download citation
DOI: https://doi.org/10.1007/978-3-319-52941-7_58
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-52940-0
Online ISBN: 978-3-319-52941-7
eBook Packages: EngineeringEngineering (R0)