Gene Expression Analysis via Spatial Clustering and Evaluation Indexing
نویسندگان
چکیده
The density-based spatial clustering for applications with noise (DBSCAN) is one of the most popular in data mining, and it used to identify useful patterns interesting distributions underlying data. Aggregation methods classifying nonlinear aggregated In particular, DNA methylations, gene expression. That show differentially skewed by distance sites grouped nonlinearly cancer daisies change Situations excretion on it. Under these conditions, DBSCAN expected have a desirable feature i that can be results changes. This research reviews compares its performance other algorithms, such as traditional number clustering, K-mean particle swarm optimization (PSO), Grey–Wolf (GWO). method offers high improvement. algorithm also better clusters gives assessment according shown this study.
منابع مشابه
Evaluation and optimization of clustering in gene expression data analysis
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ژورنال
عنوان ژورنال: Iraqi journal for computer science and mathematics
سال: 2022
ISSN: ['2788-7421']
DOI: https://doi.org/10.52866/ijcsm.2023.01.01.004