International Journal of Innovative Technology and Research
Abstract
Uncertain data contains the specific uncertainty. Uncertain data is usually found in the area of sensor networks. To find the uncertain data is very expensive. Many of the algorithms have been proposed for handling the uncertain data such as k-means, uk means, global kernel k-means, u-rule and Fuzzy c-means. However, most of previous approaches try to cluster the dataset, whereas the overlap data is not well treated. In this paper, we propose two novel active learning algorithms: 1) k-mode for classifying the certain and uncertain dataset in a whole dataset, 2) Priority R-Tree clustering the certain and uncertain data for each domain. They handle both supervised and unsupervised dataset. These techniques improve the robustness and accuracy of the clustering outcome to a great extent. By minimizing the expected error with respect to the optimal classifier, experimental results display the cluster using the Gas sensor array drift Dataset
