Journal Article

A Novel Feature Representation and Clustering for Histogram-Valued Data

Zhao, Qing, Wang, Huiwen

2025

Overview

In an era where large-scale data are produced and collected rapidly, great interest is attributed to symbolic data analysis in order to explore connotative and significant information from massive data. Recently, novel statistical techniques for histogram-valued data have been proposed and widely applied in various fields where traditional methods are not suitable. However, existing research has to face challenges in modeling posed by the complicated expression and intrinsic constraints of histogram-valued data. In this work, we introduce a novel representation for a histogram, by means of capturing the location and shape information of the corresponding probability distribution. And on this basis, an effective graph clustering method is developed to partition multivariate histogram-valued data by learning a high-quality similarity matrix. Simulation experiments and empirical case analysis demonstrate the proposed method significantly facilitates the clustering effect for histogram-valued data and presents obvious advantages compared with competing approaches.

• Publisher: MDPI AG
• Subject: Algorithms / Analysis / Clustering / clustering analysis / Data analysis / Datasets / Distribution (Probability theory) / feature representation / histogram-valued data / Histograms / Information management / Methods / Multivariate analysis / Representations / Statistical analysis / symbolic data analysis / Variables