Cyclone identification using Fuzzy C Mean clustering

In this paper, the performance of the cyclone identification system using histogram of wind speed and wind direction from the QuikSCAT satellite is demonstrated. The detections based on support vector machines (SVM) classification and Fuzzy C-Means (FCM) clustering are evaluated. SVM technique makes use of a kernel function for classification, which performs well with datasets having nonlinear boundaries. However, it is difficult to determine the suitable kernel function for each dataset and it is needed to be examined. On the other hand, FCM technique is soft unsupervised clustering, which allows each data element to be in more than one cluster with different membership value. This makes it robust to ambiguity datasets. A database of 90 events; 45 cyclone events and 45 non-cyclone events; from the QuikSCAT satellite data is used for the performance evaluation. The performance of the proposed cyclone identification system is then compared to that of [7]. The experimental results show that cyclone identification using Fuzzy C-Mean clustering outperforms that using SVM technique since the SVM is sensitive to the outliers or noises in the dataset thus leads to a reduction in identification performance.