Title :
An Improved Algorithm for Subspace Clustering Applied to Image Segmentation
Author :
Boulemnadjel, Amel ; Hachouf, Fella
Author_Institution :
Dept. d´´Electron., Univ. Mentouri de Constantine, Constantine, Algeria
Abstract :
This paper presents a new algorithm for subspace clustering for high dimensional data. It is an iterative algorithm based on the minimization of an objective function. A major weakness of subspace clustering algorithms is that almost all of them are developed based on within- class information only or by employing both within-cluster and between- clusters information. The density of cluster is lost. The new function is developed by integrating the separation and compactness of clusters. The density of cluster is introduced also in the compactness term. The experimental results confirm that the proposed algorithm gives good results on different types of images by optimizing the runtime.
Keywords :
image segmentation; iterative methods; minimisation; pattern clustering; between-clusters information; cluster density; compactness term; high dimensional data clustering; image segmentation; iterative algorithm; objective function minimization; runtime optimization; subspace clustering algorithm; within-class information; within-cluster information; Clustering algorithms; Clustering methods; Data mining; Feature extraction; Image segmentation; Linear programming; Runtime; between clusters; density; runtime; subspace clustering; within cluster;
Conference_Titel :
Information Visualisation (IV), 2012 16th International Conference on
Conference_Location :
Montpellier
Print_ISBN :
978-1-4673-2260-7