Performance Analysis and Architecture of a Clustering Hybrid Algorithm Called FA+GA-DBSCAN Using Artificial Datasets

Juan Carlos Perafan-Lopez, Valeria Lucía Ferrer-Gregory, César Nieto-Londoño, Julián Sierra-Pérez

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a widely used algorithm for exploratory clustering applications. Despite the DBSCAN algorithm being considered an unsupervised pattern recognition method, it has two parameters that must be tuned prior to the clustering process in order to reduce uncertainties, the minimum number of points in a clustering segmentation MinPts, and the radii around selected points from a specific dataset Eps. This article presents the performance of a clustering hybrid algorithm for automatically grouping datasets into a two-dimensional space using the well-known algorithm DBSCAN. Here, the function nearest neighbor and a genetic algorithm were used for the automation of parameters MinPts and Eps. Furthermore, the Factor Analysis (FA) method was defined for pre-processing through a dimensionality reduction of high-dimensional datasets with dimensions greater than two. Finally, the performance of the clustering algorithm called FA+GA-DBSCAN was evaluated using artificial datasets. In addition, the precision and Entropy of the clustering hybrid algorithm were measured, which showed there was less probability of error in clustering the most condensed datasets.

    Original languageEnglish
    Article number875
    JournalEntropy
    Volume24
    Issue number7
    DOIs
    StatePublished - Jul 2022

    Bibliographical note

    Funding Information:
    Funding: The present work was funded by the Centro de Investigación para el Desarrollo y la Innovación CIDI from Universidad Pontificia Bolivariana Sede Central (No. 636B-06/16–57).

    Publisher Copyright:
    © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

    Keywords

    • clustering
    • DBSCAN
    • entropy
    • factor analysis
    • genetic algorithm
    • pattern recognition

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