Example Concept: One effective approach is to use DBSCAN (Density-Based Spatial Clustering of Applications with Noise), which is a clustering algorithm that identifies clusters based on the density of data points. Unlike k-means, DBSCAN does not require specifying the number of clusters in advance and can effectively identify outliers as noise, making it suitable for noisy datasets. Additionally, preprocessing steps like outlier detection and removal, as well as dimensionality reduction techniques such as PCA (Principal Component Analysis), can help improve clustering outcomes by reducing the impact of noise.