基于知识量加权的直觉模糊均值聚类方法.

Aiming at the differences in the contribution of feature data to the cluster centers in the clustering algorithm and the sensitivity of the algorithm to the initial cluster centers, this paper proposes a Weighted Knowledge Amountbased Intuitionistic Fuzzy C-means Method（WKAIFCM）. Firstly, this method fuzzed the original dataset intuitionistically and improved the latest intuitionistic fuzzy knowledge measure to calculate the knowledge amount which utilized in the feature weighting of dataset. Secondly, this method initialized the cluster centers to improve the calculation accuracy and clustering efficiency of high-dimensional feature dataset by kernel space density and kernel distance. Finally, based on the principle of sample distance between clusters and the principle of minimum knowledge amount, this method established a clustering optimization model to get the optimal iterative algorithm. The experimental results based on the UCI artificial dataset show that the proposed method can greatly improve the accuracy and iterative efficiency of clustering. The classification accuracy and execution efficiency are increased by 10.63% and 31.75% respectively, and this method has good universality and stability. This paper introduces a new theory of knowledge measure into fuzzy clustering for the first time and gets extraordinary results, which creates a new case for the potential application of this theory in other related fields. [ABSTRACT FROM AUTHOR]