In this study we have implemented the Kmeans and Kmediods algorithms in order to make a practical comparison between them. The system was tested using a manual set of clusters that consists from 242 predefined clustering documents. The results showed a good indication about using them especially for Kmediods. The average precision and recall for Kmeans compared with Kmediods are 0.56, 0.52, 0.6...

Aiming at the problems of traditional K-means clustering algorithm, such as local optimal solution and slow speed caused by uncertainty k value randomness initial cluster center selection, this paper proposes an improved KMeans method. The algorithm first uses idea elbow rule based on sum squares errors to obtain appropriate number clusters k, then variance a measure degree dispersion samples, ...

Diabetic retinopathy and glaucoma are one of the major cause of blindness. Early stage segmentation and detection of optic disc may be of great help to ophthalmologist for treatment of patient before any serious complications. In this paper new methodology is proposed for the detection of the optical disk from the retinal images. Input image is first preprocessed using spatial average filtering...

We present in this paper a new ant based approach named AntClass for data clustering. This algorithm uses the stochastic principles of an ant colony in conjunction with the deterministic principles of the Kmeans algorithm. It first creates an initial partition using an improved ant-based approach, which does not require any information on the input data (such as the number of classes, or an ini...

The optimal solution is output as the result to Kmeans algorithm initial clustering center, and proposed linear distance model used complete clustering. Combined with theory of target management, focusing on job requirements responsibilities counselors, counselors’ performance appraisal objectives were determined, counselor system was established, first-level indicators second-level their weigh...

Mining knowledge from large amounts of spatial data is known as spatial data mining. It becomes a highly demanding field because huge amounts of spatial data have been collected in various applications ranging from geo-spatial data to bio-medical knowledge. The amount of spatial data being collected is increasing exponentially. So, it far exceeded human’s ability to analyze. Recently, clusterin...

Noise removal is a vital role in medical imaging, such as in magnetic resonance imaging (MRI). So in order to preserve the important features and to guarantee the correct diagnosis, the authors have proposed a new method for removing noise based on NL-mean filter and diffusion tensor. This paper presents a comparison of the MRI slices images segmentation extracted from a some 3D denoised techni...

This paper focuses on the enhanced initial centroids for the K-means algorithm. The original kmeans is using the random choice of init ial seeds which is a major limitation of the orig inal K-means algorithm because it produces less reliab le result of clustering the data. The enhanced method of the k-means algorithm includes the computation of the weighted mean to improve the centroids initial...

Clustering method is divided into hierarchical clustering, partitioning clustering, and more. K-Means algorithm is one of partitioning clustering methods and is adequate to cluster a lot of data rapidly and easily. The problem is it is too dependent on initial centers of clusters and needs the time of allocation and recalculation. We compare random method, max average distance method and triang...