A Hybrid Flower Pollination–K-Means Framework for Optimal Image Feature Selection and Clustering
DOI:
https://doi.org/10.59992/IJSR.2026.v5n9p6الكلمات المفتاحية:
Image Clustering، Feature Selection، Flower Pollination Algorithm، K-Means، Medical Image Analysisالملخص
Image clustering is an important task in computer vision, pattern recognition, medical image analysis, and image retrieval systems, but the presence of redundant of and irrelevant capacities in high-dimensional image datasets often degrades the clustering performance and enhances the Algebraic Hybrid F-Hybrid (FPA-FS + K-Means) Clustering Framework The flower pollination algorithm is used as an envelope-mainly based optimization technique to identify informative feature subsets, while K-Means is used to implement the final clustering technique. The proposed framework transformed evaluated the use of Alzheimer’s MRI image dataset containing 6,400 hundred pixels represented by means of ninety-six extracted features Experimental results confirmed that the proposed technique reduced the detectable region from 39 to 99 features well. Furthermore, the proposed framework ended up with a fairly general clustering of overall performance among all the comparison methods, achieving an accuracy of 0.3761, a normalized mutual information (NMI) of zero, and a silhouette coefficient of 0.1814. The results reveal that the flower pollination algorithm can effectively drop redundant features, improve the high quality of the cluster, and reduce the computational complexity, so that the proposed FPA-FS + K-Means framework represents a promising technique for high-dimensional photo-clustering applications.
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