Bandingkan metode

	Penambangan Pola Muncul	Penambangan Aturan Asosiasi (Apriori)
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	1999	1994
Pencetus	Guozhu Dong & Jinyan Li	Rakesh Agrawal & Ramakrishnan Srikant
Tipe	Supervised pattern discovery	Unsupervised pattern discovery algorithm
Sumber perintis	Dong, G., & Li, J. (1999). Efficient mining of emerging patterns: Discovering trends and differences. ACM SIGKDD, 43–52.	Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD, 207–216.
Alias	EP Mining, Contrast Pattern Mining, Differential Pattern Mining, Yükselen Örüntü Madenciliği	Market Basket Analysis, Frequent Itemset Mining, Birliktelik Kuralı Madenciliği, Itemset Association Analysis
Terkait	3	3
Ringkasan	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.	Association Rule Mining is an unsupervised data-mining technique that discovers co-occurrence patterns among items in transactional datasets. Formally introduced by Agrawal, Imieliński, and Swami in 1993, and refined with the landmark Apriori algorithm by Agrawal and Srikant in 1994, it identifies rules of the form X ⇒ Y — meaning that transactions containing itemset X tend to also contain itemset Y — quantified by support, confidence, and lift.