Enhancing the performance of deep learning models with fuzzy c-means clustering

Abstract

Deep learning models (DLMs), such as recurrent neural networks (RNN), long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), and gated recurrent unit (GRU), are superior for sequential data analysis due to their ability to learn complex patterns. This paper proposes enhancing performance of these models by applying fuzzy c-means (FCM) clustering on sequential data from a nonlinear plant and the stock market. FCM clustering helps to organize the data into clusters based on similarity, which improves the performance of the models. Thus, the proposed fuzzy c-means recurrent neural network (FCM-RNN), fuzzy c-means long short-term memory (FCM-LSTM), fuzzy c-means bidirectional long short-term memory (FCM-Bi-LSTM), and fuzzy c-means gated recurrent unit (FCM-GRU) models showed enhanced prediction results than RNN, LSTM, Bi-LSTM, and GRU models, respectively. This enhancement is validated using performance metrics such as root-mean-square error and mean absolute error and is further illustrated by scatter plots comparing actual versus predicted values for training, validation, and testing data. The experiment results confirm that integrating FCM clustering with DLMs shows the superiority of the proposed models.