An Improved Fuzzy Clustering Method for Text Mining

In recent years, the text data of text mining has gradually become a new research topic. Among them, the study of the text clustering has attracted wide attention. This paper proposes an improved fuzzy clustering-text clustering method based on the fuzzy C-means clustering algorithm and the edit distance algorithm. We use the feature evaluation to reduce the dimensionality of high-dimensional text vector. Because the clustering results of the traditional fuzzy C-means clustering algorithm lack the stability, we introduce the high-power sample point set, the field radius and weight. Due to the boundary value attribution of the traditional fuzzy C-means clustering algorithm, we recommend the edit distance algorithm. The results show that the improved algorithm is applied to the text clustering, making the results of clustering more stable and accurate than the traditional FCM clustering algorithm.