  Cluster assignment of large and complex images is a crucial but challenging
task in pattern recognition and computer vision. In this study, we explore the
possibility of employing fuzzy clustering in a deep neural network framework.
Thus, we present a novel evolutionary unsupervised learning representation
model with iterative optimization. It implements the deep adaptive fuzzy
clustering (DAFC) strategy that learns a convolutional neural network
classifier from given only unlabeled data samples. DAFC consists of a deep
feature quality-verifying model and a fuzzy clustering model, where deep
feature representation learning loss function and embedded fuzzy clustering
with the weighted adaptive entropy is implemented. We joint fuzzy clustering to
the deep reconstruction model, in which fuzzy membership is utilized to
represent a clear structure of deep cluster assignments and jointly optimize
for the deep representation learning and clustering. Also, the joint model
evaluates current clustering performance by inspecting whether the re-sampled
data from estimated bottleneck space have consistent clustering properties to
progressively improve the deep clustering model. Comprehensive experiments on a
variety of datasets show that the proposed method obtains a substantially
better performance for both reconstruction and clustering quality when compared
to the other state-of-the-art deep clustering methods, as demonstrated with the
in-depth analysis in the extensive experiments.
