手法を比較

	出現パターンマイニング	FP成長 (頻出パターン成長)	Rule Induction
分野	機械学習	機械学習	機械学習
系統	Machine learning	Machine learning	Machine learning
提唱年	1999	2000	1995
提唱者	Guozhu Dong & Jinyan Li	Jiawei Han, Jian Pei & Yiwen Yin	William W. Cohen
種類	Supervised pattern discovery	Frequent-itemset mining algorithm	Supervised rule learning algorithm
原典	Dong, G., & Li, J. (1999). Efficient mining of emerging patterns: Discovering trends and differences. ACM SIGKDD, 43–52.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.	Cohen, W. W. (1995). Fast effective rule induction. Proceedings of the 12th International Conference on Machine Learning, 115–123.
別名	EP Mining, Contrast Pattern Mining, Differential Pattern Mining, Yükselen Örüntü Madenciliği	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme	RIPPER, Propositional Rule Learning, Kural Tümevarımı, Inductive Rule Learning
関連	3	4	2
概要	Emerging Pattern Mining (EPM) is a contrast-based data mining technique that identifies itemsets whose support increases significantly — or jumps from zero — when moving from one dataset (or class) to another. Introduced by Dong and Li in 1999, it is primarily used in classification, anomaly detection, and trend analysis tasks where discovering discriminative patterns between two populations or time periods is the central objective.	FP-Growth, introduced by Jiawei Han, Jian Pei, and Yiwen Yin in 2000, mines frequent itemsets from transaction data without generating candidate sets, the costly step that slows the classic Apriori algorithm. It compresses the database into a frequent-pattern tree (FP-tree) in two scans, then grows frequent patterns recursively from that structure, making it dramatically faster than Apriori on large, dense datasets.	Rule Induction, and specifically the RIPPER (Repeated Incremental Pruning to Produce Error Reduction) algorithm, is a supervised machine learning method that learns a compact set of IF-THEN classification rules from labeled training data. Introduced by William W. Cohen in 1995, RIPPER applies a separate-and-conquer strategy combined with minimum description length (MDL) pruning to generate rules that are both accurate and interpretable, making it a landmark algorithm in the field of inductive rule learning.