[[["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。"],[[["The k-means clustering algorithm groups data points into clusters by minimizing the distance between each point and its cluster's centroid."],["K-means is efficient, scaling as O(nk), making it suitable for large datasets in machine learning, unlike hierarchical clustering methods."],["The algorithm iteratively refines clusters by recalculating centroids and reassigning points until convergence or a stopping criteria is met."],["Due to random initialization, k-means can produce varying results; running it multiple times and selecting the best outcome based on quality metrics is recommended."],["K-means assumes data is composed of circular distributions, which may not be accurate for all real-world data containing outliers or density-based clusters."]]],[]]