A new semi-supervised algorithm using silhouette width for clustering arbitrary-scaled data

Soumita Modak

doi:10.1080/03610926.2026.2708272

August 18, 2026Communication in Statistics- Theory and Methods

A new semi-supervised algorithm using silhouette width for clustering arbitrary-scaled data

View at Publisher

Ask AI

Bookmark

Share

Key Points

To develop a semi-supervised clustering algorithm leveraging silhouette width and an appropriate distance measure to accurately partition arbitrary-scaled and high-dimensional data.
Designed a semi-supervised clustering framework incorporating silhouette width to utilize partial class-membership information.
Integrated a customized distance measure to ensure robust clustering performance across arbitrary-scaled measurements.
Evaluated clustering effectiveness against well-established benchmark methods across diverse datasets, including high-dimensional spaces.
The proposed silhouette-based algorithm outperformed established benchmark clustering techniques across tested datasets.
The method demonstrated robust clustering accuracy and adaptability in high-dimensional data settings.

Ask about this paper

Answers are grounded in this paper and cite their sources.

Implication

Algorithm evaluation demonstrates improved partitioning across arbitrary-scaled and high-dimensional datasets, indicating superior accuracy over established clustering methods.

Abstract

In order to explore the information embedded in a given set of data, clustering has been successfully used to partition an unlabeled sample into meaningful classes. When we have any additional information regarding the original class-membership for a subset of the data either directly or indirectly, we should definitely use them in an attempt to carry out a better cluster analysis, which is known as semi-supervised clustering. This paper proposes such an algorithm, using the silhouette width, with the involvement of an appropriate distance measure that enforces its applicability to arbitrary-scaled measurements. It is shown to outperform the other well-established methods by the means of data study, with diverse applications including high-dimensional cases.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Soumita Modak (2026) studied this question.

synapsesocial.com/papers/6a86b5318a91293e6a1cc7ce https://doi.org/10.1080/03610926.2026.2708272

View at Publisher

Ask AI

Bookmark

Share

A new semi-supervised algorithm using silhouette width for clustering arbitrary-scaled data

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

A new semi-supervised algorithm using silhouette width for clustering arbitrary-scaled data

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Keep reading

Keep reading