[null,null,["最后更新时间 (UTC)：2025-06-09。"],[[["\u003cp\u003eMany clustering algorithms have a complexity of O(n^2), making them impractical for large datasets, while the k-means algorithm scales linearly with a complexity of O(n).\u003c/p\u003e\n"],["\u003cp\u003eClustering approaches include centroid-based, density-based, distribution-based, and hierarchical clustering, each suited for different data distributions and structures.\u003c/p\u003e\n"],["\u003cp\u003eCentroid-based clustering, particularly k-means, is efficient for grouping data into non-hierarchical clusters based on the mean of data points, but is sensitive to initial conditions and outliers.\u003c/p\u003e\n"],["\u003cp\u003eDensity-based clustering connects areas of high data density, effectively discovering clusters of varying shapes, but struggles with clusters of differing densities and high-dimensional data.\u003c/p\u003e\n"],["\u003cp\u003eDistribution-based clustering assumes data follows specific distributions (e.g., Gaussian), assigning points based on probability, while hierarchical clustering creates a tree of clusters, suitable for hierarchical data.\u003c/p\u003e\n"]]],[],null,[]]
