手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| DBSCAN× | 主成分分析× | |
|---|---|---|
| 分野 | 機械学習 | 機械学習 |
| 系統 | Machine learning | Machine learning |
| 提唱年≠ | 1996 | 2002 |
| 提唱者≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) |
| 種類≠ | Density-based clustering algorithm | Unsupervised dimensionality reduction |
| 原典≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231. link ↗ | Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI ↗ |
| 別名≠ | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform |
| 関連 | 3 | 3 |
| 概要≠ | DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes. | Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures. |
| ScholarGateデータセット ↗ |
|
|