Clustering is a fundamental challenge in many data-driven application fields and machine learning techniques. The data distribution determines the quality of outcomes, which has significant impact on clustering performance. As result, deep neural networks can be used to learn more accurate representations for clustering. Many recent studies have focused employing develop clustering-friendly rep...

This research aimed to discriminate between 2 general approaches to unsupervised category learning, one based on learning explicit correlational rules or associations within a stimulus domain (autocorrelation) and the other based on inventing separate categories to capture the correlational structure of the domain (category invention). An "attribute-listing" paradigm was used to index unsupervi...