Augmentation of degranulation mechanism for high-dimensional data with a multi-round optimization strategy

Abstract

Various fuzzy clustering-based granulation–degranulation techniques have been developed for constructing and optimizing information granules, which help reveal the underlying structure of experimental data in Granular Computing (GrC). Basically, a well-performing granulation–degranulation mechanism runs with a low degranulation (reconstruction) error. However, the increasingly high-dimensional characteristics of data bring great challenges to achieve accurate of reconstruction of high-dimensional data. As such, for the reconstruction of high-dimensional data, an important issue is how to reduce the reconstruction error such that the data could be reconstructed more accurately. In order to address the challenge of unacceptable high reconstruction error posed by the increase in data dimensions and improve the inefficient fuzzy clustering-based granulation in existing techniques, this study develops a multi-round iterative optimization strategy with the use of Fuzzy C-Means (FCM) to enhance reconstruction performance for high-dimensional data. First, we propose a Feature Sampling-based FCM (FS-FCM) scheme served as the granulation mechanism in the framework. The proposed scheme draws on the idea of ensemble learning, where the granulation of original high-dimensional data is accomplished by generating and training low-dimensional sub-datasets through multiple times of feature random sampling. Then, a multi-round iterative granulation–degranulation mechanism is proposed along with its algorithmic framework. Within the proposed framework, we attempt to reduce the reconstruction error by iteratively reconstructing the residual data generated in each round of granulation–degranulation. Finally, we validate the developed strategy framework over twelve publicly available datasets with varying dimension scales. A set of ablation experiments verifies the effectiveness of the FS-FCM granulation scheme. The near-perfect reconstruction performance achieved by the proposed iterative framework on the given datasets further demonstrates its superiority.