Volume - 7 | Issue - 2 | june 2025
Published
27 May, 2025
Microarray gene expression is a technique used to monitor the expression of thousands of genes under various conditions. Clustering, an unsupervised learning technique, is employed to classify or identify similar genes by grouping sets of data objects into subclasses. This approach reveals patterns that may be obscured within extensive gene datasets and complex biological networks. Processing large-dimensional genomic datasets presents inherent complexities. To address this, the proposed method reduces the dimensionality of microarray gene datasets through a combination of feature selection and feature projection, thereby enhancing the performance of clustering algorithms. The gene datasets are processed using the Python programming language, and the output is the accuracy percentage of the validated clusters. This method has been validated using several standard datasets.
KeywordsAgglomerative Clustering Feature Selection Feature Projection Model Evaluation
