Cluster Identification and Characterization of Physical Fields

TL;DR

This paper introduces a versatile clustering technique for analyzing complex physical fields from experimental or simulation data, utilizing hierarchical structures and fast search algorithms applicable across various dimensions.

Contribution

The paper presents a novel hierarchical clustering method with efficient search algorithms that can be extended to different spatial dimensions and types of physical data.

Findings

Successfully applied to 2D and 3D random data sets

Effective in identifying and characterizing physical clusters

Algorithm is dimension-independent and adaptable

Abstract

The description of complex configuration is a difficult issue. We present a powerful technique for cluster identification and characterization. The scheme is designed to treat with and analyze the experimental and/or simulation data from various methods. Main steps are as follows. We first divide the space using face or volume elements from discrete points. Then, combine the elements with the same and/or similar properties to construct clusters with special physical characterizations. In the algorithm, we adopt administrative structure of hierarchy-tree for spatial bodies such as points, lines, faces, blocks, and clusters. Two fast search algorithms with the complexity are realized. The establishing of the hierarchy-tree and the fast searching of spatial bodies are general, which are independent of spatial dimensions. Therefore, it is easy to extend the skill to other fields. As a verification and validation, we treated with and analyzed some two-dimensional and three-dimensional random data.