\(k\)が低い場合は、異なる初期値で k 平均法を複数回実行し、最良の結果を選択することで、この依存関係を軽減できます。 \(k\)が増加すると、より優れた初期重心を選択するためにk 平均法シードが必要になります。k 平均法シードについて詳しくは、M.Emre Celebi、Hassan A. Kingravi、Patricio A. Vela。
[[["わかりやすい","easyToUnderstand","thumb-up"],["問題の解決に役立った","solvedMyProblem","thumb-up"],["その他","otherUp","thumb-up"]],[["必要な情報がない","missingTheInformationINeed","thumb-down"],["複雑すぎる / 手順が多すぎる","tooComplicatedTooManySteps","thumb-down"],["最新ではない","outOfDate","thumb-down"],["翻訳に関する問題","translationIssue","thumb-down"],["サンプル / コードに問題がある","samplesCodeIssue","thumb-down"],["その他","otherDown","thumb-down"]],["最終更新日 2025-02-25 UTC。"],[[["\u003cp\u003eK-means clustering is generally efficient and useful for large datasets, but it has drawbacks regarding its sensitivity to initial centroid values and difficulty handling varying data densities and outliers.\u003c/p\u003e\n"],["\u003cp\u003eGeneralizing k-means can improve performance on complex datasets with varying cluster characteristics, though this requires more advanced techniques.\u003c/p\u003e\n"],["\u003cp\u003eChoosing the optimal number of clusters (k) remains a manual process and significantly impacts the results.\u003c/p\u003e\n"],["\u003cp\u003eHigh-dimensional data can pose challenges for k-means due to the "curse of dimensionality," which can be mitigated using dimensionality reduction techniques like PCA and spectral clustering.\u003c/p\u003e\n"],["\u003cp\u003eOutliers can distort k-means results, suggesting pre-processing steps like outlier removal or clipping for improved performance.\u003c/p\u003e\n"]]],[],null,[]]