Semnan University Press Journal of Modeling in Engineering 2008-4854 23 80 2025 03 21 An Improved Deep Text Clustering via Local Manifold of an Autoencoder Embedding An Improved Deep Text Clustering via Local Manifold of an Autoencoder Embedding 307 324 9376 10.22075/jme.2024.32399.2565 FA Fatemeh Daneshfar Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran Amin Golzari Oskouei Department of Computer Engineering, University of Tabriz, Tabriz, Iran Maryam Dorosti Department of Electrical and Computer Engineering, Kharazmi University, Tehran, Iran Mohammad Javad Aghajani Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran Journal Article 2023 11 21 Text clustering is a method for separating specific information from textual data and can even classify text according to topic and sentiment, which has drawn much interest in recent years. Deep clustering methods are especially important among clustering techniques because of their high accuracy. These methods include two main components: dimensionality reduction and clustering. Many earlier efforts have employed autoencoder for dimension reduction; however, they are unable to lower dimensions based on manifold structures, and samples that are like one another are not necessarily placed next to one another in the low dimensional. In the paper, we develop a Deep Text Clustering method based on a local Manifold in the Autoencoder layer (DCTMA) that employs multiple similarity matrices to obtain manifold information, such that this final similarity matrix is obtained from the average of these matrices. The obtained matrix is added to the bottleneck representation layer in the autoencoder. The DCTMA's main goal is to generate similar representations for samples belonging to the same cluster; after dimensionality reduction is achieved with high accuracy, clusters are detected using an end-to-end deep clustering. Experimental results demonstrate that the suggested method performs surprisingly well in comparison to current state-of-the-art methods in text datasets. Text clustering is a method for separating specific information from textual data and can even classify text according to topic and sentiment, which has drawn much interest in recent years. Deep clustering methods are especially important among clustering techniques because of their high accuracy. These methods include two main components: dimensionality reduction and clustering. Many earlier efforts have employed autoencoder for dimension reduction; however, they are unable to lower dimensions based on manifold structures, and samples that are like one another are not necessarily placed next to one another in the low dimensional. In the paper, we develop a Deep Text Clustering method based on a local Manifold in the Autoencoder layer (DCTMA) that employs multiple similarity matrices to obtain manifold information, such that this final similarity matrix is obtained from the average of these matrices. The obtained matrix is added to the bottleneck representation layer in the autoencoder. The DCTMA's main goal is to generate similar representations for samples belonging to the same cluster; after dimensionality reduction is achieved with high accuracy, clusters are detected using an end-to-end deep clustering. Experimental results demonstrate that the suggested method performs surprisingly well in comparison to current state-of-the-art methods in text datasets. https://modelling.semnan.ac.ir/article_9376_e3fecd130d97e2d623637c66e0c0c503.pdf