Abstract
Clustering is one of the most valuable methods of computational intelligence field, in particular, in human–Computer Systems Interaction context, in which sets of related objects are cataloged into clusters. In this background, we put a spotlight on the importance of the clustering exploit in the competence computing for Case Based Reasoning (CBR) systems. For that, we apply an efficient clustering technique, named “Soft DBSCAN” that combines Density-Based Clustering of Application with Noise (DBSCAN) and fuzzy set theory, on competence model. Our clustering method is galvanized by Fuzzy C Means in the way of using the fuzzy membership functions. The results of our method show that it is efficient not only in handling noises, contrary to Fuzzy C Means, but also, able to assign one data point into more than one cluster, and in particular it shows high accuracy for predicting the competence of CBR. Simulative experiments are carried out on a variety of datasets, throughout different evaluation’s criteria, which emphasize the soft DBSCAN’s success and cluster validity to check the good quality of clustering results and its usefulness in the competence of the CBR.
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References
Asuncion A, Newman D (2007) UCI machine learning repository. http://www.ics.uci.edu/mlearn
Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of 2nd international conference on knowledge discovery, pp 226–231
Filzmoser P, Garrett RG, Reimann C (2005) Multivariate outlier detection in exploration geochemistry. Comput Geosci 31:579–587
Fridgen JJ, Kitchen NR, Sudduth KA, Drummond ST, Wiebold WJ, Fraisse CW (2004) Management zone analyst (MZA): software for subfield management zone delineation. Agron J 96:100–108
Frigui H, Krishnapuram R (1996) A robust clustering algorithm based on competitive agglomeration and soft rejection of outliers. In: IEEE computer society, pp 550–555
Guill A, Gonzalez J, Rojas I, Pomares H, Herrera LJ, Valenzuela O, Prieto A (2007) Using fuzzy logic to improve a clustering technique for function approximation. Neurocomputing 70(16–18):2853–2860
Han J (2005) Data mining concepts and techniques. Morgan Kaufmann Publishers Inc, San Francisco, CA
Klir GJ, Yuan B (1995) Fuzzy sets and fuzzy logic theory and applications. Prentice Hall PTR, Upper Saddle River
MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In Proceedings of 5th Berkeley symposium on mathematical statistics and probability, vol 1, pp 281–297
Parker L, Kandel A (2010) Scalable fuzzy neighborhood dbscan. In: FUZZ-IEEE, pp 1–8
Smiti A, Elouedi Z (2012a) Dbscan-gm: an improved clustering method based on gaussian means and dbscan techniques. In: Proceedings of international conference on intelligent engineering systems, pp 573–578
Smiti A, Elouedi Z (2012b) Competence and performance-improving approach for maintaining case-based reasoning systems. In: Proceedings of international conference on computational intelligence and information technology, pp 356–361
Smiti A, Elouedi Z (2013a) Soft DBSCAN: improving DBSCAN clustering method using fuzzy set theory. In: Proceedings of 6th international conference on human system interaction, pp 380–385
Smiti A, Elouedi Z (2013b) Modeling competence for case based reasoning systems using clustering. In: Proceedings of 26th international conference Florida artificial intelligence research society, pp 399–404
Smyth B, McKenna E (2001) Competence models and the maintenance problem. Comput Intell 17(2):235–249
Zadeh L (1965) Fuzzy sets. Inf Control 8(3):338–353
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Smiti, A., Elouedi, Z. (2014). Competence of Case-Based Reasoning System Utilizing a Soft Density-Based Spatial Clustering of Application with Noise Method. In: S. Hippe, Z., L. Kulikowski, J., Mroczek, T., Wtorek, J. (eds) Issues and Challenges in Artificial Intelligence. Studies in Computational Intelligence, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-319-06883-1_5
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