[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["没有我需要的信息","missingTheInformationINeed","thumb-down"],["太复杂/步骤太多","tooComplicatedTooManySteps","thumb-down"],["内容需要更新","outOfDate","thumb-down"],["翻译问题","translationIssue","thumb-down"],["示例/代码问题","samplesCodeIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC)：2024-07-22。"],[[["Many 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)."],["Clustering approaches include centroid-based, density-based, distribution-based, and hierarchical clustering, each suited for different data distributions and structures."],["Centroid-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."],["Density-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."],["Distribution-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."]]],[]]
