research-article

Improvement of Fuzzy KNN Classification Algorithm Based on Fuzzy C-means

Authors:

Kun Yu,

Yushui Geng,

Xuemei Li,

Mengjie YangAuthors Info & Claims

CSAE '18: Proceedings of the 2nd International Conference on Computer Science and Application Engineering

Article No.: 93, Pages 1 - 5

https://doi.org/10.1145/3207677.3278080

Published: 22 October 2018 Publication History

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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.

References

[1]

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.

Crossref

Google Scholar

[2]

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

[3]

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

[4]

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.

Crossref

Google Scholar

[5]

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.

Crossref

Google Scholar

[6]

T. M. Cover, P. E. Hart. 1967. Nearest neighbor pattern classification. IEEE Trans.inf.theory, 13 (1)(1967), 21--27.

Digital Library

Google Scholar

[7]

R. Bellman, R. Kalaba, and L. Zadeh. 1966. Abstraction and pattern classification. Journal of Mathematical Analysis & Applications, 13(1)(1966), 1--7.

Google Scholar

[8]

E. H. Ruspini. 1969. A New Approach to Clustering. Information and Control, 1(15)(1969), 22--32.

Google Scholar

[9]

J. C. Bezdek. 1976. "Feature Selection for Binary DataMedical Diagnosis with Fuzzy Sets". National Computer Conference, 1976.

Google Scholar

[10]

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

[11]

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

[12]

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.

Digital Library

Google Scholar

[13]

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

Cited By

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Arisandi CZarlis MEfendi S(2021)The Analysis of Fuzzy C-Means and Pearson Correlation Methods for Data Reduction in kNN Algorithm2021 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA)10.1109/DATABIA53375.2021.9650228(13-21)Online publication date: 11-Nov-2021
https://doi.org/10.1109/DATABIA53375.2021.9650228
Patikar SSaha PNeogy SChowdhury C(2020)An Approach towards prediction of Diabetes using Modified Fuzzy K Nearest Neighbor2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON)10.1109/GUCON48875.2020.9231066(73-76)Online publication date: 2-Oct-2020
https://doi.org/10.1109/GUCON48875.2020.9231066

Index Terms

Improvement of Fuzzy KNN Classification Algorithm Based on Fuzzy C-means
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification

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Published In

CSAE '18: Proceedings of the 2nd International Conference on Computer Science and Application Engineering

October 2018

1083 pages

ISBN:9781450365123

DOI:10.1145/3207677

Conference Chair:
Ali Emrouznejad,
Program Chair:
Zhihong Qian

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 October 2018

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CSAE '18

CSAE '18: The 2nd International Conference on Computer Science and Application Engineering

October 22 - 24, 2018

Hohhot, China

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CSAE '18 Paper Acceptance Rate 189 of 383 submissions, 49%;

Overall Acceptance Rate 368 of 770 submissions, 48%

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Cited By

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Arisandi CZarlis MEfendi S(2021)The Analysis of Fuzzy C-Means and Pearson Correlation Methods for Data Reduction in kNN Algorithm2021 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA)10.1109/DATABIA53375.2021.9650228(13-21)Online publication date: 11-Nov-2021
https://doi.org/10.1109/DATABIA53375.2021.9650228
Patikar SSaha PNeogy SChowdhury C(2020)An Approach towards prediction of Diabetes using Modified Fuzzy K Nearest Neighbor2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON)10.1109/GUCON48875.2020.9231066(73-76)Online publication date: 2-Oct-2020
https://doi.org/10.1109/GUCON48875.2020.9231066

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Rough intuitionistic fuzzy C-means algorithm and a comparative analysis

Interval-valued possibilistic fuzzy C-means clustering algorithm

A modified intuitionistic fuzzy c-means algorithm incorporating hesitation degree