基于模糊C-Means的改进型KNN分类算法

KNN算法是一种思想简单且容易实现的分类算法，但在训练集较大以及特征属性较多时候，其效率低、时间开销大.针对这一问题，论文提出了基于模糊C-means的改进型KNN分类算法，该算法在传统的KNN分类算法基础上引入了模糊C-means理论，通过对样本数据进行聚类处理，用形成的子簇代替该子簇所有的样本集，以减少训练集的数量，从而减少KNN分类过程的工作量、提高分类效率，使KNN算法更好地应用于数据挖掘.通过理论分析和实验结果表明，论文所提算法在面对较大数据时能有效提高算法的效率和精确性，满足处理数据的需求.

Improved KNN classification algorithm based on Fuzzy C-Means

KNN algorithm is a classification algorithm that is simple and easy to implement， but when the training set is rather big and features are more， its efficiency is low with which takes more time. To solve this problem， an improved KNN classification algorithm was proposed based on Fuzzy C-Means. The improved algorithm introduces the theory of Fuzzy C-Means based on the traditional KNN classification algorithm. Through processing the sample data clustering， the formation of sub clusters substitutes all the sample set of the sub cluster， which helps reduce the number of training set. Thereby the workload of the KNN classification process is reduced， with the classification efficiency improved and the KNN algorithm better applied in data mining. The theoretical analysis and experimental results show that this method is able to significantly improve the efficiency and accuracy of the algorithm when dealing with large data， meeting the demand of processing data.