Knowledge Management Based on Fuzzy Clustering Algorithm with Picard Iteration

Knowledge management of concepts was essential in educational environment. The popular fuzzy c-means algorithm (FCM) converges to a local minimum of the objective function. Fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. A Fuzzy C-Means algorithm based on Mahalanobis distance (FCM-M) was proposed to improve those limitations of GG and GK algorithms, but it is not stable enough when some of its covariance matrices are not equal. Hence, different initializations may lead to different results. The important issue is how to avoid getting a bad local minimum value to improve the cluster accuracy. The particle swarm optimization (PSO) is a popular and robust strategy for optimization problems. But the main difficulty in applying PSO to real-world applications is that PSO usually need a large number of fitness evaluations before a satisfying result can be obtained. A new improved Fuzzy Clustering Algorithm with Picard Iteration is proposed. In this paper, the improved new algorithm, Use the best performance of clustering algorithm in data analysis and interpretation. Each cluster of data can easily describe features of knowledge structures. Manage the knowledge structures of Concepts to construct the model of features in the pattern recognition completely.