Abstract
The density-based spatial clustering for applications with noise (DBSCAN) is one of the most popularapplications of clustering in data mining, and it is used to identify useful patterns and interesting distributions in theunderlying data. Aggregation methods for classifying nonlinear aggregated data. In particular, DNA methylations,gene expression. That show the differentially skewed by distance sites and grouped nonlinearly by cancer daisiesand the change Situations for gene excretion on it. Under these conditions, DBSCAN is expected to have a desirableclustering feature i that can be used to show the results of the changes. This research reviews the DBSCAN andcompares its performance with other algorithms, such as the traditional number of clustering, K-mean particle swarmoptimization (PSO), and Grey–Wolf optimization (GWO). This method offers high performance for improvement.The DBSCAN algorithm also offers better results of clusters and gives better performance assessment according tothe results shown in this study
Recommended Citation
Salman, Awrad D.
(2023)
"Gene Expression Analysis via Spatial Clustering and EvaluationIndexing,"
Iraqi Journal for Computer Science and Mathematics: Vol. 4:
Iss.
1, Article 4.
DOI: https://doi.org/10.52866/ijcsm.2023.01.01.004
Available at:
https://ijcsm.researchcommons.org/ijcsm/vol4/iss1/4
