KC-Means: A Fast Fuzzy Clustering
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 fi...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Advances in Fuzzy Systems |
| Online Access: | http://dx.doi.org/10.1155/2018/2634861 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832566685823926272 |
|---|---|
| author | Israa Abdzaid Atiyah Adel Mohammadpour S. Mahmoud Taheri |
| author_facet | Israa Abdzaid Atiyah Adel Mohammadpour S. Mahmoud Taheri |
| author_sort | Israa Abdzaid Atiyah |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7965c184b69645618209e91d0f490a80 |
| institution | Kabale University |
| issn | 1687-7101 1687-711X |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Fuzzy Systems |
| spelling | doaj-art-7965c184b69645618209e91d0f490a802025-02-03T01:03:24ZengWileyAdvances in Fuzzy Systems1687-71011687-711X2018-01-01201810.1155/2018/26348612634861KC-Means: A Fast Fuzzy ClusteringIsraa Abdzaid Atiyah0Adel Mohammadpour1S. Mahmoud Taheri2Faculty of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, IranFaculty of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, IranSchool of Engineering Science, College of Engineering, University of Tehran, Tehran, IranA 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.http://dx.doi.org/10.1155/2018/2634861 |
| spellingShingle | Israa Abdzaid Atiyah Adel Mohammadpour S. Mahmoud Taheri KC-Means: A Fast Fuzzy Clustering Advances in Fuzzy Systems |
| title | KC-Means: A Fast Fuzzy Clustering |
| title_full | KC-Means: A Fast Fuzzy Clustering |
| title_fullStr | KC-Means: A Fast Fuzzy Clustering |
| title_full_unstemmed | KC-Means: A Fast Fuzzy Clustering |
| title_short | KC-Means: A Fast Fuzzy Clustering |
| title_sort | kc means a fast fuzzy clustering |
| url | http://dx.doi.org/10.1155/2018/2634861 |
| work_keys_str_mv | AT israaabdzaidatiyah kcmeansafastfuzzyclustering AT adelmohammadpour kcmeansafastfuzzyclustering AT smahmoudtaheri kcmeansafastfuzzyclustering |