K C -Means: A Fast Fuzzy Clustering

Joint Authors

Abdzaid Atiyah, Israa
Mohammadpour, Adel
Taheri, S. Mahmoud

Source

Advances in Fuzzy Systems

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

Mathematics

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