Clustering analysis of massive data in wireless multimedia sensor networks (WMSN) has\nbecome a hot topic. However, most data clustering algorithms have difficulty in obtaining latent\nnonlinear correlations of data features, resulting in a low clustering accuracy. In addition, it is\ndifficult to extract features from missing or corrupted data, so incomplete data are widely used in\npractical work. In this paper, the optimally designed variational autoencoder networks is proposed\nfor extracting features of incomplete data and using high-order fuzzy c-means algorithm (HOFCM)\nto improve cluster performance of incomplete data. Specifically, the feature extraction model is\nimproved by using variational autoencoder to learn the feature of incomplete data. To capture\nnonlinear correlations in different heterogeneous data patterns, tensor based fuzzy c-means algorithm\nis used to cluster low-dimensional features. The tensor distance is used as the distance measure\nto capture the unknown correlations of data as much as possible. Finally, in the case that the\nclustering results are obtained, the missing data can be restored by using the low-dimensional\nfeatures. Experiments on real datasets show that the proposed algorithm not only can improve the\nclustering performance of incomplete data effectively, but also can fill in missing features and get\nbetter data reconstruction results.
Loading....