A data scientist is tasked with segmenting a large dataset of customer locations for a retail chain. The dataset contains geospatial coordinates. A preliminary visualization reveals that customer locations form dense, non-spherical clusters of varying sizes in urban centers, while numerous sparse, isolated points correspond to customers in rural areas. The business objective is to identify the core, high-density market areas and explicitly label the sparse, outlying customer locations as noise for separate analysis. Which clustering algorithm is most suitable for this specific task?
K-medoids clustering
K-means clustering
Hierarchical clustering
DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
The correct answer is DBSCAN (Density-Based Spatial Clustering of Applications with Noise). The scenario describes two key requirements: the clusters are non-spherical, and there is a need to identify and separate noise (outliers). DBSCAN is a density-based algorithm specifically designed to handle these two conditions. It can find arbitrarily shaped clusters and has a built-in mechanism for classifying points in low-density regions as noise.
K-means clustering is not suitable because it assumes clusters are spherical and isotropic (of similar variance). It also forces every data point into a cluster, meaning it cannot inherently label outliers as noise.
Hierarchical clustering, while capable of handling non-spherical clusters to some degree, does not have an explicit, built-in mechanism for identifying noise in the same way DBSCAN does. Every point is assigned to a cluster in the hierarchy, and separating noise would require an additional, often manual, step.
K-medoids clustering is a variation of K-means that is more robust to outliers in the calculation of cluster centers, but it still assigns every point to a cluster and struggles with non-spherical cluster shapes, making it less suitable than DBSCAN for this scenario.
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Why is K-means not suitable for identifying noise points?