Application of Fuzzy C-Means Algorithm in Fuzzy Classification of Japanese Resource Database

Times are developing and progressing, while traditional database text classification methods have gradually failed to meet the needs of personalization and dynamic changes. In this paper, we propose a fuzzy classification method based on Fuzzy C-Means Clustering Algorithm (FCM) algorithm for Japanese language resource databases, which aims to improve classification accuracy and enhance user retrieval experience. By comparing the performance of FCM with K-means algorithm in terms of clustering accuracy, profile coefficient, Rand index and F1 value, this paper demonstrates the superiority of FCM algorithm in fuzzy classification of Japanese language resource database. The experimental results show that the average classification accuracy of the FCM algorithm is 89.152%, which is significantly higher than the 64.9% of K-means, and the contour coefficients and the Rand index also show the advantages of the FCM algorithm in terms of clustering cohesion and consistency with the actual labels. The high performance of the F1 value further confirms that the FCM algorithm achieves a better balance between precision and recall. The research in this paper not only provides a new perspective and solution to the fuzzy classification problem of Japanese language resource databases, but also contributes to the development and innovation of database classification technology.