Advanced Fuzzy C-Means Algorithm Based on Local Density and Distance

This paper presents an advanced fuzzy C-means (FCM) clustering algorithm to overcome the weakness of the traditional FCM algorithm, including the instability of random selecting of initial center and the limitation of the data separation or the size of clusters. The advanced FCM algorithm combines t...

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Vydané v:Shanghai jiao tong da xue xue bao Ročník 23; číslo 5; s. 636 - 642
Hlavní autori: Wu, Shaochun, Pang, Yijie, Shao, Sen, Jiang, Keyuan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Shanghai Shanghai Jiaotong University Press 01.10.2018
Springer Nature B.V
Predmet:
Algorithms
Architecture
Clustering
Computer Science
Density
Electrical Engineering
Engineering
Life Sciences
Materials Science
Objective function
Stability
degree of membership
TP 399
fuzzy C-means (FCM) clustering algorithm
clustering center
objective function
ISSN:1007-1172, 1995-8188
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Popis
Shrnutí:This paper presents an advanced fuzzy C-means (FCM) clustering algorithm to overcome the weakness of the traditional FCM algorithm, including the instability of random selecting of initial center and the limitation of the data separation or the size of clusters. The advanced FCM algorithm combines the distance with density and improves the objective function so that the performance of the algorithm can be improved. The experimental results show that the proposed FCM algorithm requires fewer iterations yet provides higher accuracy than the traditional FCM algorithm. The advanced algorithm is applied to the influence of stars’ box-office data, and the classification accuracy of the first class stars achieves 92.625%.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1007-1172
1995-8188
DOI:10.1007/s12204-018-1993-y