Paper Info
Title
Improvement Of Fuzzy Knn Classification Algorithm Based On Fuzzy C-Means
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.
Year
DOI
Venue
2018
10.1145/3207677.3278080
PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND APPLICATION ENGINEERING (CSAE2018)
Keywords
DocType
Citations 
Fuzzy C-means, Fuzzy KNN, membership
Conference
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Kun Yu1387.33
Yushui Geng244.44
Xuemei Li300.34
Mengjie Yang400.34