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...

This work presents a kernel method for clustering the nodes of a weighted, undirected, graph. The algorithm is based on a two-step procedure. First, the sigmoid commute-time kernel (KCT), providing a similarity measure between any couple of nodes by taking the indirect links into account, is computed from the adjacency matrix of the graph. Then, the nodes of the graph are clustered by performin...

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...

Feature Selection in large multi-dimensional data sets is becoming increasingly important for several real world applications. One such application, used by network administrators, is Network Intrusion Detection. The major problem with anomaly based intrusion detection systems is high number of false positives. Motivated by such a requirement, we propose sv(M)kmeans: a two step hybrid feature s...

The aim of the project is to detect and classify the brain tumor from MRI image. This project involves mainly 6 stages namely Input Image, Preprocessing, Segmentation, Post Processing, Feature Extraction and Classification. In this phase, 4 stages are implemented, Input image, preprocessing, segmentation and post processing. Input image reads the MRI brain image. Preprocessing mainly includes i...

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...