K C -Means: A Fast Fuzzy Clustering
Joint Authors
Abdzaid Atiyah, Israa
Mohammadpour, Adel
Taheri, S. Mahmoud
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-06-03
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
A novel hybrid clustering method, named KC-Means clustering, is proposed for improving upon the clustering time of the Fuzzy C-Means algorithm.
The proposed method combines K-Means and Fuzzy C-Means algorithms into two stages.
In the first stage, the K-Means algorithm is applied to the dataset to find the centers of a fixed number of groups.
In the second stage, the Fuzzy C-Means algorithm is applied on the centers obtained in the first stage.
Comparisons are then made between the proposed and other algorithms in terms of time processing and accuracy.
In addition, the mentioned clustering algorithms are applied to a few benchmark datasets in order to verify their performances.
Finally, a class of Minkowski distances is used to determine the influence of distance on the clustering performance.
American Psychological Association (APA)
Abdzaid Atiyah, Israa& Mohammadpour, Adel& Taheri, S. Mahmoud. 2018. K C -Means: A Fast Fuzzy Clustering. Advances in Fuzzy Systems،Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-986255
Modern Language Association (MLA)
Abdzaid Atiyah, Israa…[et al.]. K C -Means: A Fast Fuzzy Clustering. Advances in Fuzzy Systems No. 2018 (2018), pp.1-8.
https://search.emarefa.net/detail/BIM-986255
American Medical Association (AMA)
Abdzaid Atiyah, Israa& Mohammadpour, Adel& Taheri, S. Mahmoud. K C -Means: A Fast Fuzzy Clustering. Advances in Fuzzy Systems. 2018. Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-986255
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-986255