Abstract: Definition of the samples will directly impact on the accuracy and the efficiency of KNN.In view of disadvantages to the traditional KNN algorithm on the distance the definition and categories of decision,proposed the use of attribute importance to category to improve KNN algorithm (FCD-KNN).At first,a distance of the two samples is defined as the correlation distance of the same attribute values．The distance can effectively measure the similarity degree of the two sample．Secondly,According to this distance selects the k nearest neighbors．Finally,the category of the test sample is decided by the average distance and the numbers on the respective category．The theoretical analysis and the simulation experiment show that compared with KNN and-KNN,raised the rate of accuracy enormously in classification.

Key words: KNN algorithm,Correlation distances,Attribute,Sample distance mechanism