Internet traffic data categorization using particle of swarm optimization algorithm
Nikita Shrivastava, Amit Dubey · 2016
The clustering technique plays an important role in data mining process. For the mining of internet traffic data faced a lot of problem of noise and internet traffic number of iteration. The process of pattern generation used two type of technique such as supervised learning and unsupervised learning. In unsupervised learning clustering process are used. The varieties of clustering technique are used such as k-means, FCM and constraints clustering technique. The constraints clustering technique gives the two solution approach one is seed selection and another is mapping of seed in terms of constraint of center. In this paper modified the seed selection process using genetic algorithm technique. The genetic algorithm process select variable value one is seed value and another is constraint of center value. In constraints cluster technique used some value of center and generates new center value of new cluster for the better generation of cluster. For more improvement of constraints clustering technique used two level constraints clustering technique for better improvement of cluster technique. In this dissertation modified the constraints clustering technique for improvement. In the process of improvement used genetic algorithm technique. Genetic algorithm technique gives the better selection of seed for internet traffic database. For the performance evaluation of proposed algorithm used three real time dataset from UCI machine learning center. The proposed algorithm implemented in MATLAB software and measures some standard parameter for the validation of proposed methodology.