Near-infrared spectroscopy (NIRS) is a fast and non-destructive analytical method.
Associated with chemometrics, it becomes a powerful tool for the pharmaceutical industry …

An integrated framework for density-based cluster analysis, outlier detection, and data
visualization is introduced in this article. The main module consists of an algorithm to …

The formation of red blood cells begins with the differentiation of multipotent haematopoietic
progenitors. Reconstructing the steps of this differentiation represents a general challenge in …

Instance Space Analysis (ISA) is a recently developed methodology to (a) support objective
testing of algorithms and (b) assess the diversity of test instances. Representing test …

Over the last several years, DBSCAN (Density-Based Spatial Clustering of Applications with
Noise) has been widely used in many areas of science due to its simplicity and the ability to …

Clustering image pixels is an important image segmentation technique. While a large
amount of clustering algorithms have been published and some of them generate …

Density peaks clustering algorithm (DP) has difficulty in clustering large-scale data, because
it requires the distance matrix to compute the density and-distance for each object, which …

It is crucial to determine the optimal number of clusters for the clustering quality in cluster
analysis. From the standpoint of sample geometry, two concepts, ie, the sample clustering …

The density peak clustering algorithm treats local density peaks as cluster centers, and
groups non-center data points by assuming that one data point and its nearest higher …

Point cloud data segmentation, filtering, classification, and feature extraction are the main
focus of point cloud data processing. DBSCAN (density-based spatial clustering of …