Deep Multi-View Clustering Optimized by Long Short-Term Memory Network

Long short-term memory (LSTM) networks have shown great promise in sequential data analysis, especially in time-series and natural language processing. However, their potential for multi-view clustering has been largely underexplored. In this paper, we introduce a novel approach called deep multi-view clustering optimized by long short-term memory network (DMVC-LSTM), which leverages the sequential modeling capability of LSTM to effectively integrate multi-view data. By capturing complex interdependencies and nonlinear relationships between views, DMVC-LSTM improves clustering accuracy and robustness. The method includes three feature fusion techniques—concatenation, averaging, and attention-based fusion—with concatenation as the primary method. Notably, DMVC-LSTM is well suited for datasets that exhibit symmetry, as it can effectively handle symmetrical relationships between views while preserving the underlying structures. Extensive experiments demonstrate that DMVC-LSTM outperforms existing multi-view clustering algorithms, particularly in high-dimensional and complex datasets, achieving superior performance in datasets like 20 Newsgroups and Wikipedia Articles. This paper presents the first application of LSTM in multi-view clustering, marking a significant step forward in both clustering performance and the application of LSTM in multi-view data analysis.