Clustering is a widely used technique in data analysis, but outliers can significantly impact the accuracy of traditional clustering methods. To address this issue, a new clustering technique is proposed that incorporates outliers during clustering. The proposed approach involves using a variable, (λ > 0), to define the cluster radius. Weighted and Unweighted clustering methods are proposed for clustering considering the outliers. Centroids are calculated without using the direct K-means algorithm. Evaluation on standard datasets, such as Fisher Iris, shows improved performance compared to K-means and DBSCAN, with a maximum Silhouette score of 0.568 and an accuracy of 88% even in the presence of outliers.