FCM and aiNet methods in data cluster
Tang Xian-ying · Jisuanji gongcheng yu sheji · 2004
When using an Euclidean distance measure, fuzzy c-means clustering did not work well if the underlying classes or clusters deviated strongly from hyperspherical structures. An artificial network structure (aiNet) stressing the clonal selection and affinity maturation and immune network theory was capable of reducing redundancy, describing data structure, including the shape of clusters. The characters of two methods were compared by experiments, and results show that aiNet appears excellent adaptability when the underlying classes or clusters from convex set.