Document Type

Article

Source of Publication

International Journal on Semantic Web and Information Systems

Publication Date

5-23-2024

Abstract

In this work, a simple yet robust neighboring-aware hierarchical-based clustering approach (NHC) is developed. NHC employs its dynamic technique to take into account the surroundings of each point when clustering, making it extremely competitive. NHC offers a straightforward design and reliable clustering. It comprises two key techniques, namely, neighboring- aware and filtering and merging. While the proposed neighboring-aware technique helps find the most coherent clusters, filtering and merging help reach the desired number of clusters during the clustering process. The NHC's performance, which includes all evaluation metrics and run time, has been thoroughly tested against nine clustering rivals using four similarity measures on several real-world numerical and textual datasets. The evaluation is done in two phases. First, we compare NHC to three common clustering methods and show its efficacy through empirical analysis. Second, a comparison with six relevant, contemporary competitors highlights NHC's extremely competitive performance.

ISSN

1552-6291

Publisher

IGI Global

Volume

20

Issue

1

First Page

1

Last Page

24

Disciplines

Computer Sciences

Keywords

Hierarchical clustering, Neighboring-aware technique, Filtering and merging, Clustering rivals, Similarity measures

Indexed in Scopus

no

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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