Application of Rosette Pattern for Clustering and Determining the Number of Cluster
Clustering is one of the most important research topics which has many practical applications such as medical imaging and Non-Destructive Testing (NDT). Most clustering algorithms like K-means, fuzzy C-Means (FCM) and their derivatives require the number of clusters as one of the initializing para...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Stefan cel Mare University of Suceava
2011-08-01
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| Series: | Advances in Electrical and Computer Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.4316/AECE.2011.03013 |
| _version_ | 1818937551488024576 |
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| author | SADR, A. MOMTAZ, A. K. |
| author_facet | SADR, A. MOMTAZ, A. K. |
| author_sort | SADR, A. |
| collection | DOAJ |
| description | Clustering is one of the most important research topics which has many practical applications such as medical imaging and Non-Destructive Testing (NDT). Most clustering algorithms like K-means, fuzzy C-Means (FCM) and their derivatives require the number of clusters as one of the initializing parameters. This paper proposes an algorithm for image clustering with no need to any initializing parameter. In this state-of-the-art, an image is sampled based on a rosette pattern and according to the pattern characteristics, the extracted samples are clustered and then the number of clusters is determined. The centroids of classes are computed by means of a method based on calculation of distribution function. Based on different data sets, the results show that the algorithm improves the capability of the clustering by a minimum of 62.26% and 87.62% in comparison with FCM and K-means algorithms, respectively. Moreover, in dealing with high resolution data sets, the efficiency of the algorithm in clusters detection and run time improvement increases considerably. |
| first_indexed | 2024-12-20T05:53:45Z |
| format | Article |
| id | doaj.art-f5c5a83f2e3d408990a161223e1999c5 |
| institution | Directory Open Access Journal |
| issn | 1582-7445 1844-7600 |
| language | English |
| last_indexed | 2024-12-20T05:53:45Z |
| publishDate | 2011-08-01 |
| publisher | Stefan cel Mare University of Suceava |
| record_format | Article |
| series | Advances in Electrical and Computer Engineering |
| spelling | doaj.art-f5c5a83f2e3d408990a161223e1999c52022-12-21T19:51:06ZengStefan cel Mare University of SuceavaAdvances in Electrical and Computer Engineering1582-74451844-76002011-08-01113778410.4316/AECE.2011.03013Application of Rosette Pattern for Clustering and Determining the Number of ClusterSADR, A.MOMTAZ, A. K.Clustering is one of the most important research topics which has many practical applications such as medical imaging and Non-Destructive Testing (NDT). Most clustering algorithms like K-means, fuzzy C-Means (FCM) and their derivatives require the number of clusters as one of the initializing parameters. This paper proposes an algorithm for image clustering with no need to any initializing parameter. In this state-of-the-art, an image is sampled based on a rosette pattern and according to the pattern characteristics, the extracted samples are clustered and then the number of clusters is determined. The centroids of classes are computed by means of a method based on calculation of distribution function. Based on different data sets, the results show that the algorithm improves the capability of the clustering by a minimum of 62.26% and 87.62% in comparison with FCM and K-means algorithms, respectively. Moreover, in dealing with high resolution data sets, the efficiency of the algorithm in clusters detection and run time improvement increases considerably.http://dx.doi.org/10.4316/AECE.2011.03013clusteringFuzzy C-means (FCM)pattern recognitionRosette Patternvalidity index |
| spellingShingle | SADR, A. MOMTAZ, A. K. Application of Rosette Pattern for Clustering and Determining the Number of Cluster Advances in Electrical and Computer Engineering clustering Fuzzy C-means (FCM) pattern recognition Rosette Pattern validity index |
| title | Application of Rosette Pattern for Clustering and Determining the Number of Cluster |
| title_full | Application of Rosette Pattern for Clustering and Determining the Number of Cluster |
| title_fullStr | Application of Rosette Pattern for Clustering and Determining the Number of Cluster |
| title_full_unstemmed | Application of Rosette Pattern for Clustering and Determining the Number of Cluster |
| title_short | Application of Rosette Pattern for Clustering and Determining the Number of Cluster |
| title_sort | application of rosette pattern for clustering and determining the number of cluster |
| topic | clustering Fuzzy C-means (FCM) pattern recognition Rosette Pattern validity index |
| url | http://dx.doi.org/10.4316/AECE.2011.03013 |
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