What type of study is this?

This is a Validation Study study.

What question did this study set out to answer?

This research evaluates semantic clustering algorithms to determine effective methods for word embeddings in NLP applications.

March 14, 2026International Journal of Information Management Data InsightsOpen Access

A comparative empirical evaluation of semantic clustering algorithms on static word embeddings

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Authors

AAAsefeh Asemi RSRajab Kiani Shahvandy MHMahdi Houshangi

Discussion

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Overview

Comparative analysis reveals K-means and K-medoids optimize information management systems, suggesting effective strategies for clustering approaches.

Key Points

This research evaluates semantic clustering algorithms to determine effective methods for word embeddings in NLP applications.
Empirical evaluation of K-means, K-medoids, and DBSCAN on GloVe and Wiki embeddings
Performance assessed using Silhouette Score and Davies-Bouldin Index
Principal Component Analysis used for visualization
Validations conducted on a corpus of 303 research articles
K-means with GloVe embeddings yields the most semantically coherent clusters
DBSCAN excels in outlier detection but underperforms in thematic clustering
K-medoids showed robustness against outliers with less compact groupings

Cite This Study

Asemi et al. (2026) studied this question.

synapsesocial.com/papers/69b4b9fb18185d8a398024f6 https://doi.org/10.1016/j.jjimei.2026.100396

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March 14, 2026International Journal of Information Management Data InsightsOpen Access

A comparative empirical evaluation of semantic clustering algorithms on static word embeddings

View Full Paper

Ask AI

Bookmark

Authors

AAAsefeh Asemi RSRajab Kiani Shahvandy MHMahdi Houshangi

Discussion

Member takes

Overview

Comparative analysis reveals K-means and K-medoids optimize information management systems, suggesting effective strategies for clustering approaches.

Key Points

This research evaluates semantic clustering algorithms to determine effective methods for word embeddings in NLP applications.
Empirical evaluation of K-means, K-medoids, and DBSCAN on GloVe and Wiki embeddings
Performance assessed using Silhouette Score and Davies-Bouldin Index
Principal Component Analysis used for visualization
Validations conducted on a corpus of 303 research articles
K-means with GloVe embeddings yields the most semantically coherent clusters
DBSCAN excels in outlier detection but underperforms in thematic clustering
K-medoids showed robustness against outliers with less compact groupings

Cite This Study

Asemi et al. (2026) studied this question.

synapsesocial.com/papers/69b4b9fb18185d8a398024f6 https://doi.org/10.1016/j.jjimei.2026.100396

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