Subspace clustering is an important unsupervised clustering approach. It is based on the
assumption that the high-dimensional data points are approximately distributed around …

Text clustering aims to organize a vast collection of documents into meaningful and coherent
clusters, thereby facilitating the extraction of valuable insights. While current frameworks for …

Applications of electric heating, which can improve carbon emission reduction and
renewable energy utilization, have brought new challenges to the safe operation of energy …

Liquid storage tanks play a vital role in the modern chemical process industry (CPI). The
strong ground motion caused by large-scale earthquakes may easily impose severe …

In existing deep clustering methods, it is assumed that all generated representations are
equally important during the clustering procedure. However, if the model can't learn proper …

Unsupervised dimensionality reduction (DR) aims to preserve input data structure in a low-
dimensional (LD) space based on neighborhood information. In contrast, supervised DR …

Abstract Support Vector Machine (SVM) is one of the successful methods of machine
learning. SVM uses kernel tricks to efficiently learn non-linear classification tasks. The kernel …

Neighborhood-based and density-based clustering methods are applied in various data
analysis applications. However, most of them have low performance in mixed …

The present paper focuses on semi-supervised classification problems. Semi-supervised
learning is a learning task through both labeled and unlabeled samples. One of the main …

In machine learning, nearest neighbor (NN) regression is one of the most prominent
methods for numeric prediction. It estimates the output variable of a new data point by …