K-Means Clustering

K-Means Clustering is an unsupervised machine learning algorithm that partitions a dataset into k pre-specified clusters by minimizing the within-cluster sum of squared distances from each point to its assigned cluster centroid. The algorithm iteratively assigns each data point to the nearest centroid and then recalculates centroids as the mean of all points in each cluster until convergence. Despite its simplicity, K-Means is remarkably effective and remains one of the most widely used clustering algorithms across domains. Geospatial Applications of K-MeansIn remote sensingRemote SensingRemote sensing is the science of collecting data about Earth's surface without direct physical contact, primarily usi..., K-Means performs unsupervised classificationUnsupervised ClassificationUnsupervised classification is an automated remote sensing technique that groups pixels into spectral clusters withou... of satellite imagerySatellite ImagerySatellite imagery consists of photographs and data captured by Earth observation satellites orbiting the planet. Thes... by grouping pixels with similar spectral signatures into clusters that correspond to land cover types. This approach, called ISODATA when extended with splitting and merging, is valuable when no labeled training data is available. Location intelligence analysts use K-Means to segment customers by geographic and behavioral features, identifying spatially coherent market segments. Urban planners cluster neighborhoods by socioeconomic and infrastructure indicators to identify areas with similar characteristics. K-Means also groups point-of-interest data to discover activity centers and spatial patterns in mobility dataMobility DataMobility data consists of anonymized location observations from mobile devices that capture how people move through g.... Considerations and LimitationsK-Means requires specifying the number of clusters k in advance, which may not be obvious for geographic datasets where natural groupings vary across regions. The algorithm assumes spherical, equally-sized clusters and can produce poor results when clusters have irregular shapes or widely varying densities, which is common in spatial data. Initialization sensitivity means different random starting points can yield different results, though techniques like K-Means++ improve initialization. For geospatial dataGeospatial DataGeospatial data encompasses information about the location, shape, and relationships of physical features on Earth. I..., incorporating spatial coordinates as features alongside thematic attributes requires careful feature scaling to balance spatial and non-spatial dimensions.