A MODIFIED KOHONEN SELF-ORGANIZING MAP (KSOM) CLUSTERING FOR FOUR CATEGORICAL DATA
DOI:
https://doi.org/10.11113/jt.v78.9275Keywords:
Kohonen Self-Organizing Map (KSOM), clustering, categorical data, Ant Colony Optimization (ACO)Abstract
The Kohonen Self-Organizing Map (KSOM) is one of the Neural Network unsupervised learning algorithms. This algorithm is used in solving problems in various areas, especially in clustering complex data sets. Despite its advantages, the KSOM algorithm has a few drawbacks; such as overlapped cluster and non-linear separable problems. Therefore, this paper proposes a modified KSOM that inspired from pheromone approach in Ant Colony Optimization. The modification is focusing on the distance calculation amongst objects. The proposed algorithm has been tested on four real categorical data that are obtained from UCI machine learning repository; Iris, Seeds, Glass and Wisconsin Breast Cancer Database. From the results, it shows that the modified KSOM has produced accurate clustering result and all clusters can clearly be identified.
References
R. Rojas. 1996. Unsupervised Learning and Clustering Algorithms. in Neural Networks. 99–121.
J. a Kangas, T. K. Kohonen, and J. T. Laaksonen. 1990. Variants of self-organizing maps. IEEE Trans. Neural Netw. Berlin: Springer Berlin Heidelberg. Jan 1990. 1(1): 93–9.
T. Kohonen. 2000. Self-organizing Maps of Massive Document Collections. In Neural Comput. New Challenges Perspect. New Millenn. Proc. IEEE-INNS-ENNS Int. Jt. Conf. Neural Networks. 2: 3–9.
V. Emamian, M. Kaveh, and A. H. Tewfik. 2003. Robust Clustering of Acoustic Emission Signals Using the Kohonen Network. EURASIP J. Appl. Signal Processing. 3: 276–286.
V. Emamian, M. Kaveh, and a. H. Tewfik. 2000. Robust clustering of acoustic emission signals using the Kohonen network. IEEE Int. Conf. Acoust. Speech, Signal Process. Proc. 6: 3891–3894.
K. Lagus, T. Honkela, and S. Kaski. 2000. WEBSOM for Textual Data Mining. 345–364.
E. A. Uriarte and F. D. MartÃn. 2005. Topology Preservation in SOM. 19–22.
S. Negri and L. A. Belanche. 2001. Heterogeneous Kohonen networks. In Connectionist Models of Neurons Learning Processes and Artificial Intelligence. 6th International WorkConference on Artificial and Natural Neural Networks IWANN 200. 243–252.
H. Merdun. 2010. Self-organizing Map Artificial Neural Network Application in Multidimensional Soil Data Analysis.
H. Yin. 2002. ViSOM - A Novel Method for Multivariate Data Projection and Structure Visualization. IEEE Trans. Neural Netw. 13(1): 237–43.
S. Wu and T. W. S. Chow. 2005. PRSOM: A New Visualization Method by Hybridizing Multidimensional Scaling and Self-Organizing Map. IEEE Trans. Neural Networks. 16(6): 1362–1380.
F. Hadzic and T. S. Dillon. 2005. CSOM: self-Organizing Map for Continuous Data. In Industrial Informatics. 3rd IEEE International Conference on, 2005 INDIN '05. 740-745.
V. Neagoe, S. Member, R. Stoica, and A. Ciurea. 2014. Concurrent Self-Organizing Maps for Supervised / Unsupervised Change Detection in Remote. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.. 7(8): 3525–3533.
V. E. Neagoe, S. V. Carata and A. D. Ciotec. 2015. Automatic Target Recognition in SAR Imagery Using Pulse-Coupled Neural Network Segmentation Cascaded With Virtual Training Data Generation CSOM-based classifier. Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International, Milan. 3274-3277.
T. Kohonen. 1982. Self-Organized Formation Of Topologically Correct Feature Maps. Biol. Cybern. 69: 59–69.
Banerjee, C. Krumpelman, and R. J. Mooney. 2005. Model-Based Overlapping Clustering. In Proceedings of the eleventh ACM SIGKDD International Conference On Knowledge Discovery In Data Mining (KDD '05). ACM, New York, NY, USA. 532-537.
S. Maps and T. Kohonen. 1996. New Developments and Applications of. 1: 164–172.
X. Liu and H. Fu. 2010. An Effective Clustering Algorithm With Ant Colony. J. Comput. Apr 2010. 5(4): 598–605.
J. Handl and B. Meyer. 2002. Improved Ant-Based Clustering and Sorting in a Document Retrieval Interface. 913–923.
J. Handl, J. Knowles, and M. Dorigo. 2004. Strategies for the Increased Robustness of Ant-Based Clustering. in Engineering Self-Organising Systems. Berlin: Springer Berlin Heidelberg. 90-104.
Bohari, Z. H., Ghani, S. A., Baharom, M. F., Nasir, M. N. M., Jali, M. H., & Thayoob, Y. H.. 2014. Feature Analysis of Numerical Calculated Data from Sweep Frequency Analysis ( SFRA ) Traces Using Self Organizing Maps. Jurnal Teknologi. 3: 37–42.
Rezaeia, Z., Kasmunia, M. D., Selamat, A., Abaeia, M. S. M. R. G., & Kadir, M. R. A.. 2014. Comparative Study Of Clustering Algorithms In Order To Virtual Histology (Vh) Image Segmentation. Jurnal Teknologi. 75(2): 133–139.
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