Feature selection using binary Harris Hawks optimization algorithm to improve K-Means clustering

This study aims to explore the suitability of adopting k-means clustering for categorization of five disaggregate data sets that undergo feature selection employing a binary Harris Hawks optimization algorithm. First, a feature selection technique is used by BHHOA to classify the most important features from each dataset prior to executing the reduction of data dimensionality and the improvement of the quality of the data collected. Then in the next step, the k-means clustering algorithm is used on the fine-tuned data sets to form a sensible number of clusters. The evaluation of k-means clustering considered the effectiveness of the clustering algorithm by its accuracy and feature set. Comparing the results with those obtained by using the same set of features but by other methods, it is clear that BHHOA enhances feature selection enhances clustering, and this confirms its capability to handle large datasets with high dimensionality. The outcomes show that using the proposed approach, consisting of BHHOA for feature selection followed by k-means clustering, could significantly improve the classification performance of the datasets.