档案管理中文本数据的增量多模态聚类方法

随着现代档案管理数据量的不断增长，有效地对档案文本进行聚类划分能够提升档案分类和检索的效率。文中提出2种增量多模态文本数据聚类方法，通过对文本内容进行多视角分析，融合挖掘文本的潜在主题特征，提升文本聚类的准确性。此外，设计文本聚类多模态增量学习模型，提升海量、动态文本划分的效率。在文本数据集上的实验结果表明，文中提出的增量多模态文本聚类方法优于单模态和多模态聚类算法，能够对文本数据进行有效划分。

Incremental multi-modal clustering methods for text data in archives administration

With the continuous growth of modern archive management data, the effective clustering of archive text can significantly improve the efficiency of archive classification and retrieval. This paper proposes two incremental multi-modal text data clustering methods. By multi-perspective analysis of the text content, the potential topic features of texts are integrated to improve the accuracy of text clustering. In addition, the corresponding incremental multi-modal feature learning models for text clustering are designed to improve the efficiency of massive and dynamic text partition. Experimental results on real-world text data sets show that the proposed incremental multimodal text clustering methods outperform the compared stated-of-the-art methods, being able to effectively classify text data.