Unsupervised Learning

Unsupervised learning is a machine-learning approach that trains models on data without labeled outputs. Its goal is to identify patterns, structures, or relationships, often through clustering or dimensionality reduction. Unlike supervised learning, it does not require target labels. Applications include market segmentation, anomaly detection, and data visualization.

Japanese terms

1. Unsupervised learning — 教師きょうしなし学習がくしゅう: A machine-learning approach in which a model learns patterns from unlabeled data.
2. Model — モデル: A mathematical representation that processes input data to produce predictions, classifications, or decisions.
3. Clustering — クラスタリング: Grouping similar data points according to their features.
4. Dimensionality reduction — 次元じげん削減さくげん: Reducing the number of features while retaining essential structure or information.
5. Target value — 目標もくひょう値ち: The expected output used in supervised learning and absent from unsupervised training data.
6. Market segmentation — 市場しじょうセグメンテーション: Dividing a market into groups with shared characteristics or behavior.
7. Anomaly detection — 異常いじょう検知けんち: Identifying unusual patterns or outliers in data.
8. Data visualization — データ可視化かしか: Representing data graphically to reveal patterns, trends, and insights.