Image Clustering based on Artificial Intelligence Techniques | ||
| AL-Rafidain Journal of Computer Sciences and Mathematics | ||
| Article 9, Volume 11, Issue 1, June 2014, Pages 99-112 PDF (520.69 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.33899/csmj.2014.163735 | ||
| Author | ||
| Baydaa Ibraheem Khaleel | ||
| College of Computer Science and Mathematics University of Mosul | ||
| Abstract | ||
| Clustering has been widely used in data analysis and pattern recognition and classification. The objective of cluster analysis is the classification of objects according to similarities among them, and organizing of data into groups. There are several algorithms for clustering large data sets or streaming data sets, Their aims are to organize a collection of data items into clusters. These such items are more similar to each other within a cluster, and different than they are in the other clusters. We have take the advantage of classification abilities of Artificial Intelligence Techniques (AITs) to classify images data set into a number of clusters. The Gath-Geva (GG) fuzzy clustering algorithm, Artificial Bee Colony algorithm(ABC), Radial Basis Function Network(RBF), and then combined Gath-Geva algorithm with (RBF) algorithm to produce Fuzzy RBF (FRBF) method were applied using images data set to classify this data set into a number of clusters (classes). Each cluster will contain data set with most similarity in the same cluster and most dissimilarity with the different clusters. We compute the classification rate, and false rate on this data set. Finally we make comparisons between results obtained after applying these algorithms on this images data set.TheFRBF is better than the other three methods that applied in this research such as G-G, ABC, RBF, because the FRBF was obtained higher classification rate in testing state equal (96.8571) and low false alarm equal(3.1429). | ||
| Keywords | ||
| Gath-Geva(GG) fuzzy clustering algorithm; Artificial Bee Colony algorithm (ABC); Radial Basis Function (RBF) Neural Network; Fuzzy RBF (FRBF) | ||
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