Image segmentation is an important stage for object recognition. Many methods have been
proposed in the last few years for grayscale and color images. In this paper, we present a …

Spatial information is often used to enhance the robustness of traditional fuzzy c-means
(FCM) clustering algorithms. Although some recently emerged improvements are …

Clustering is an important technique to deal with large scale data which are explosively
created in internet. Most data are high-dimensional with a lot of noise, which brings great …

Today, data is being generated with a high speed. Managing large volume of data has
become a challenge in the current age. Clustering is a method to analyze data that is …

Traditional machine fault diagnosis techniques are labor-intensive and hard for nonexperts
to use. In this paper, a novel three-stage intelligent fault diagnosis approach is proposed for …

Standard k-means clustering necessitates computing pairwise Euclidean distances between
each instance x in a data set D and all cluster centers, resulting in inadequate efficiency …

A novel density-based clustering algorithm, called Density Peak Clustering (DPC), has
recently received great attention due to its efficiency in clustering performance and simplicity …

Abstract In 2014, Rodriguez and Laio proposed a famous clustering algorithm based on a
fast search and find density peaks dubbed as DPC (Rodriguez and Laio, 2014). DPC has …

Abstract Density Peak Clustering (DPC) is a popular state-of-the-art clustering algorithm,
which requires pairwise (dis) similarity of data objects to detect arbitrary shaped clusters …

Deep Convolutional Neural Networks (CNN) has achieved significant success in computer
vision field. However, the high computational cost of the deep complex models prevents the …