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An efficient algorithm based on cluster analysis for exploring structure of large multivariate datasets.

Mehmet Cevri̇1

  • 1Department of Mathematics, Faculty of Science, Istanbul University, Istanbul, 34134, Turkey.

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|September 5, 2025
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Summary
This summary is machine-generated.

This study introduces an efficient algorithm for classifying Teucrium species using micromorphological data. The method enhances pharmaceutical compound discovery by accurately grouping plant species based on detailed characteristics.

Keywords:
Cluster analysisClustering validation measuresFactor analysisK-meansSilhouette coefficientTeucrium

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Area of Science:

  • Botany
  • Chemotaxonomy
  • Computational Biology

Background:

  • The Lamiaceae family, including over 260 Teucrium species, has significant pharmaceutical potential.
  • Micromorphological characters are crucial for plant classification and identifying bioactive compounds.
  • Efficient computational methods are needed for analyzing large, multivariate botanical datasets.

Purpose of the Study:

  • To develop and validate an efficient algorithm for clustering Teucrium species based on micromorphological data.
  • To compare the performance of the proposed clustering methodology against existing techniques.
  • To facilitate the identification of Teucrium species with valuable pharmaceutical compounds.

Main Methods:

  • Cluster and factor analyses were performed on 21 micromorphological characters of 40 Teucrium species.
  • The K-means clustering algorithm was optimized using the silhouette index for determining the optimal number of clusters.
  • A novel algorithm combining factor analysis and silhouette validation was developed and implemented in Mathematica.
  • The proposed methodology was evaluated through computer simulations and compared with standard clustering approaches.

Main Results:

  • The developed algorithm efficiently classified Teucrium species based on their micromorphological traits.
  • The silhouette coefficient method proved effective and accurate for validating clustering in large datasets.
  • The classification aids in identifying Teucrium species valuable for pharmaceutical manufacturing and drug development.
  • The new methodology demonstrated superior performance compared to commonly used clustering techniques.

Conclusions:

  • The combined factor analysis and silhouette validation approach offers an effective and accurate method for large-scale botanical data clustering.
  • This computational strategy enhances the classification of Teucrium species, supporting the discovery of novel pharmaceutical compounds.
  • The study highlights the importance of micromorphological data and advanced computational tools in chemotaxonomy and drug discovery.