A grid-based boundary sharpening clustering algorithm

To address the clustering problem for arbitrary shapes, in this paper, we propose a Grid-based Boundary Sharpening clustering algorithm called as “GBSharp”. This method is grounded in morphology and relies on two fundamental morphological operations: dilation and erosion. The main innovations of the...

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Bibliographic Details
Published in:Engineering applications of artificial intelligence Vol. 161; p. 112045
Main Authors: Ma, Lin, Yan, Qijing, Lv, Mengxia, Ma, Tiefeng, Cheng, Mingchang
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.12.2025
Subjects:
Dilation
Erosion
Grid-based
Inversion method
Inversion method
Dilation
Grid-based
Erosion
ISSN:0952-1976
Online Access:Get full text
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Summary:To address the clustering problem for arbitrary shapes, in this paper, we propose a Grid-based Boundary Sharpening clustering algorithm called as “GBSharp”. This method is grounded in morphology and relies on two fundamental morphological operations: dilation and erosion. The main innovations of the proposed algorithm lie in two aspects. Firstly, we further introduce the concepts of inward dilation and bridge erosion based on the basic morphological operations to reduce the impact of the chain effect. Secondly, a unique indexing structure is designed specifically for non-empty cells in high dimensional space. In addition, to tackle the complex conditional judgments encountered in high-dimensional scenarios, we further utilize the inversion method for bridge-erosion operation. Experiments conducted on synthetic datasets and real-world datasets further validate the effectiveness and efficiency of the proposed algorithm. •An adaptive binary threshold distinguishes foreground from background grid cells.•Inward dilation and bridge-erosion operations to refine boundaries between clusters.•Non-empty cell indexing structure for addressing high-dimensional issues.•Inversion method for performing high-dimensional bridge-erosion operations.•The proposed method is applicable to large-scale meteorological data.
ISSN:0952-1976
DOI:10.1016/j.engappai.2025.112045