CAJ | 학술논문

在维吾尔文文字识别中，能否有效地聚类将直接影响识别结果的好坏。为改善聚类效果，针对维吾尔文连体段聚类，提出了一种改进的K-means聚类算法。该算法首先采用等间距法多次选择类中心，然后选择最佳码本和利用有效相似比来动态调整聚类个数K，最后完成了连体段聚类。实验结果表明：与传统K-means算法相比，改进的K-means算法得到了较好聚类效果，聚类正确率达90%以上。
재유오이문문자식별중，능부유효지취류장직접영향식별결과적호배。위개선취류효과，침대유오이문련체단취류，제출료일충개진적K-means취류산법。해산법수선채용등간거법다차선택류중심，연후선택최가마본화이용유효상사비래동태조정취류개수K，최후완성료련체단취류。실험결과표명：여전통K-means산법상비，개진적K-means산법득도료교호취류효과，취류정학솔체90%이상。
In Uyghur character recognition, the effect of the cluster will affect the recognition rate directly. To improve the clustering result, an improved K-means clustering algorithm based on Uyghur word-part is presented. The first step of the method is to select the center of the clustering by using the equal interval method repeatedly in order to select the best codebook, then adjust the number of clustering classes(noted as K)by using an effective similarity ratio dynamically. Finally, the word-part clustering is completed. The experimental results show that:compared with the traditional K-means algorithm, the improved K-means algorithm gets a better result and the clustering accuracy is more than 90%.