Comparison of AIS and fuzzy c-means clustering methods on the classification of breast cancer and diabetes datasets

Turkish Journal of Electrical Engineering and Computer Sciences

Volumn 22, Issue 5, 2014, Pages 1241-1254

  Özşen, Seral ^a   Ceylan, Rahime ^a  

^a SELCUK UNIVERSITY   (Turkey)

Author keywords

Artificial immune systems; Artificial neural networks; Breast cancer dataset; Diabetes dataset; Fuzzy c means clustering

Indexed keywords

CLUSTERING ALGORITHMS; DATA REDUCTION; DISEASES; FUZZY CLUSTERING; FUZZY SYSTEMS; IMMUNE SYSTEM; NEURAL NETWORKS;

ARTIFICIAL IMMUNE SYSTEM; BREAST CANCER; DATA-REDUCTION ALGORITHMS; FUZZY C MEANS CLUSTERING; FUZZY C-MEANS CLUSTERING ALGORITHMS; FUZZY C-MEANS CLUSTERING METHOD; MACHINE LEARNING REPOSITORY; WISCONSIN BREAST CANCER DATASET;

CLASSIFICATION (OF INFORMATION);

EID: 84905980291     PISSN: 13000632     EISSN: 13036203     Source Type: Journal    
DOI: 10.3906/elk-1210-62     Document Type: Article

References (18)

1. M. Kantardzic, Data Mining: Concepts, Models, Methods and Algorithms, New York, Wiley, 2002.
2. P. Dulyakarn, Y. Rangsanseri, "Fuzzy C-means clustering using spatial information with application to remote sensing", Proceedings of the 22nd Asian Conference on Remote Sensing, 2001.
3. J. Timmis, "Artificial immune systems: a novel data analysis technique inspired by the immune network theory", PhD Dissertation, Department of Computer Science, University of Wales, U.K., 2000.
4. J. Timmis, M. Neal, "A resource limited artificial immune system for data analysis", Knowledge Based Systems, Vol. 14, pp.121-130, 2001.
5. L.N. De Castro, F.J. Von Zuben, "An evolutionary immune network for data clustering", Proceedings of the IEEE Brazilian Symposium on Artificial Neural Networks, pp. 84-89, 2000.
6. P. Vishwambhar, D. Praven, M. Prabhat, "Data clustering with artificial innate immune system adding probabilistic behaviour", International Journal of Data Mining and Emerging Technologies, Vol. 1, pp.77-84, 2011.
7. A.J. Graff, A.P. Engelnrecht, "Clustering data in an uncertain environment using an artificial immune system", Pattern Recognition Letters, Vol. 32, pp. 342-351, 2011.
8. W. Ahmad, A. Narayanan, "Population based artificial immune system clustering algorithm", Proceedings of the 10th International Conference on Artificial Immune Systems, pp. 348-360, 2011.
9. rd Annual Conference on Natural Computation, Vol. 3, pp. 647-650, 2007.
10. rd Annual Conference on Natural Computation, Vol. 3, pp. 657-661, 2007.
11. T. Liu, Y. Zhau, H. Zhifeng, "A new clustering algorithm based on artificial immune system", Proceedings of the 5th International Conference on Fuzzy Systems and Knowledge Discovery, pp. 347-351, 2008.
12. st International Workshop on Database Technology and Applications, pp. 275-278, 2009.
13. A. Secker, M.N. Davies, A.A. Freitas, J. Timmis, E. Clark, D.R. Flower, "An artificial immune system for clustering amino acids in the context of protein function classification", Journal of Mathematical Modelling and Algorithms, Vol. 8, pp. 103-123, 2009.
14. D. Dasgupta, Artificial Immune Systems: A Bibliography. Technical Report, No: CS-07-004, USA, 2007.
15. A.S. Perelson, G.F. Oster, "Theoretical studies of clonal selection: minimal antibody repertuarie size and reliability of self-nonself discrimination", Journal of Theoretical Biology, Vol. 81, pp. 645-670, 1979.
16. J.S.R. Jang, C.T. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing, Prentice Hall, USA, 1997.
17. D. Li, H. Gu, L. Zhang, "A fuzzy c-means clustering algorithm based on nearest neighbour intervals for incomplete data", Expert Systems with Applications, Vol. 37, pp. 6942-6947, 2010.
18. http://www.ifs.tuwien.ac.at/dm/somtoolbox/datasets.html, Last accessed: 1 June 2011.