无监督学习在无标签数据中查找模式和结构(没有给定的答案)— 自动发现分组、降低维度或检测异常。当您拥有数据但没有标签时使用它,以发现隐藏的结构。
无监督学习如何运作
UNSUPERVISED → learn from data WITHOUT labels (no given correct answers):
→ the algorithm finds STRUCTURE/patterns in the data on its own
→ no 'right answer' to learn from → it discovers groupings, relationships, or representations
→ for: exploring data, finding hidden structure, when labels are unavailable/expensive
主要的无监督学习任务
CLUSTERING → group similar data points into clusters:
→ e.g. customer segmentation, grouping similar documents (k-means, hierarchical, DBSCAN)
DIMENSIONALITY REDUCTION → reduce features while preserving structure:
→ e.g. PCA → compress/visualize high-dimensional data; simplify for other models
ANOMALY DETECTION → find unusual/outlier data points:
→ e.g. fraud detection, defect detection, finding rare events
ASSOCIATION → find relationships (e.g. 'people who buy X also buy Y')
