ABSTRACT
K-nearest-neighbor (KNN)1 algorithm is a kind of classification algorithm, which is simple and easy to implement. However, when there is a large number of training sets or numerous attributes, it has the disadvantage of inefficient and time consuming. In this paper, a fuzzy KNN classification algorithm based on fuzzy C-means is proposed. Based on the traditional KNN classification algorithm, this algorithm introduces the fuzzy KNN theory, and combines the fuzzy C-means theory. Improve the efficiency of fuzzy KNN classification by clustering sample data with C-means and reduce the number of training sets. The improved algorithm makes fuzzy KNN algorithm performing better on data mining. Theoretical analysis and experimental results show that the algorithm can effectively improve the efficiency and accuracy of algorithm when dealing with large amounts of data, and meet the needs of data processing.
- J. M. Keller, M. R. Gray, and J. A. Givens. 1985. A Fuzzy k-nearest Neighbour Algorithm. IEEE Transactions on System Man & Cybernetics, 4(1985), 580--585.Google ScholarCross Ref
- K. W. Tobin, S. S. Gleason, and T. P. Karnowski. 1998. Adaptation of the fuzzy k-nearest neighbor classifier for manufacturing automation. Proceedings of SPIE - The international society for optics and photonics, 3306(1998), 122--130.Google Scholar
- M. Zbancioc, and S. M. Feraru. 2013. Emotion recognition of the SROL Romanian database using fuzzy KNN algorithm. International Symposium on Electronics & Telecomm, 2013(2013), 347--350.Google Scholar
- A. H. Choksi, and S. P. Thakkar. 2012. Recognition of Similar appearing Gujarati Characters using Fuzzy-KNN Algorithm. International Journal of Computer Applications, 55(6)(2012), 12--17.Google ScholarCross Ref
- K. Josien, and T. W. Liao. 2000. Integrated use of fuzzy c-means and fuzzy KNN for GT part family and machine cell formation. International Journal of Production Research, 38(15)(2000), 3513--3536.Google ScholarCross Ref
- T. M. Cover, P. E. Hart. 1967. Nearest neighbor pattern classification. IEEE Trans.inf.theory, 13 (1)(1967), 21--27. Google ScholarDigital Library
- R. Bellman, R. Kalaba, and L. Zadeh. 1966. Abstraction and pattern classification. Journal of Mathematical Analysis & Applications, 13(1)(1966), 1--7.Google Scholar
- E. H. Ruspini. 1969. A New Approach to Clustering. Information and Control, 1(15)(1969), 22--32.Google Scholar
- J. C. Bezdek. 1976. "Feature Selection for Binary DataMedical Diagnosis with Fuzzy Sets". National Computer Conference, 1976.Google Scholar
- J. C. Bezdek, R. Ehrlich, and W. Full. 1984. FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2)(1984), 191--203.Google Scholar
- G. GUO, and D. NEAGU. 2005. Fuzzy kNNModel Applied to Predictive Toxicology Data Mining. International Journal of Computational Science and Engineering, 5(03)(2005), 321--333.Google Scholar
- T. C. Havens, J. C. Bezdek, C. Leckie, L. O. Hall, and M. Palaniswami. 2012. Fuzzy c-Means Algorithms for Very Large Data. IEEE Transactions on Fuzzy Systems, 20(6)(2012), 1130--1146. Google ScholarDigital Library
- H.B. Chen, J. U. Guo, and Y. H. Xie. 2013. KNN Fault Detection Based on Improved K-means Clustering. ShenYang University of Chemical Technology, 27(1)(2013), 69--73.Google Scholar
Index Terms
- Improvement of Fuzzy KNN Classification Algorithm Based on Fuzzy C-means
Recommendations
Rough intuitionistic fuzzy C-means algorithm and a comparative analysis
Compute '13: Proceedings of the 6th ACM India Computing ConventionData clustering algorithms are used in many fields like anonymisation of databases, image processing, analysis of satellite images and medical data analysis. There are several C-Means clustering algorithms in the literature. Besides the hard C-Means, ...
Interval-valued possibilistic fuzzy C-means clustering algorithm
Type-2 fuzzy sets have drawn increasing research attentions in the pattern recognition community, since it is capable of modeling various uncertainties that cannot be appropriately managed by usual fuzzy sets. Although it has been introduced to data ...
A modified intuitionistic fuzzy c-means algorithm incorporating hesitation degree
Highlights- We proposed mIFCM algorithm to overcome the problem of the IFCM algorithms.
- The ...
AbstractFuzzy c-means (FCM) algorithm is an unsupervised machine learning algorithm and has been used in many applications. But, FCM does not consider hesitation in the case of imprecise data. The intuitionistic fuzzy c-means (IFCM) algorithm, ...
Comments