Unsupervised Deep Learning: Taxonomy and algorithms

Abstract

Clustering is a fundamental challenge in many data-driven application fields and machine learning techniques. The data distribution determines the quality of the outcomes, which has a significant impact on clustering performance. As a result, deep neural networks can be used to learn more accurate data representations for clustering. Many recent studies have focused on employing deep neural networks to develop a clustering-friendly representation, which has resulted in a significant improvement in clustering performance. We present a systematic survey of clustering with deep learning in this study. Then, a taxonomy of deep clustering is proposed, as well as some sample algorithms for our overview. Finally, we discuss some exciting future possibilities for clustering using deep learning and offer some remarks. Povzetek: Ta članek opisuje metode globokega združevanja v skupine in predlaga taksonomijo globokega združevanja v skupine.

K-CAE: Image Classification Using Convolutional AutoEncoder Pre-Training and K-means Clustering

“…• We will also use other supervised deep learning algorithms, such as Convolutional Neural Networks (CNN), etc [13]; • With the use of large data sets, we will introduce the notion of incrementality into the database provided to the autoencoder [14]; • This architecture can also be used in certain application domains, such as handwriting recognition, with very large datasets.…”