K-means is one of the most simple and popular clustering algorithms, which implemented as
a standard clustering method in most of machine learning researches. The goal of K-means …

Feature selection has achieved unprecedented success in obtaining sparse discriminative
features. However, the existing methods almost use the-norm constraint on transformation …

Due to simplicity, K-means has become a widely used clustering method. However, its
clustering result is seriously affected by the initial centers and the allocation strategy makes …

Classification is a fundamental task in the field of data mining. Unfortunately, high-
dimensional data often degrade the performance of classification. To solve this problem …

Feature selection based on the fuzzy neighborhood rough set model (FNRS) is highly
popular in data mining. However, the dependent function of FNRS only considers the …

This article extends the expectation-maximization (EM) formulation for the Gaussian mixture
model (GMM) with a novel weighted dissimilarity loss. This extension results in the fusion of …

Feature selection is an indispensable aspect of modern machine learning, especially for
high-dimensional datasets where overfitting and computational inefficiencies are common …

Load forecasting is critical to the task of energy management in power systems, for example,
balancing supply and demand and minimizing energy transaction costs. There are many …

Nowadays, acoustic emission (AE) has been widely used to the nondestructive evaluation
(NDE) and structural health monitoring (SHM) for compressor blades. However, traditional …

The best way to represent EMG signals for classification is a topic that has been widely
studied due to the need to improve precision when identifying the type of movement being …