방법 비교

	Apriori 알고리즘	K-means 군집화	준지도 학습	Voting Ensemble
분야	머신러닝	머신러닝	머신러닝	머신러닝
계열	Machine learning	Machine learning	Machine learning	Machine learning
기원 연도	1994	1967 (formalized 1982)	1970s–2006 (formalized)	1990s–2004
창시자	Agrawal, R. & Srikant, R.	MacQueen, J. B.; Lloyd, S. P.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)	Lam & Suen; Kuncheva, L. I. (systematic treatment)
유형	Frequent itemset and association rule mining algorithm	Partitional clustering	Learning paradigm	Ensemble (combination of multiple classifiers by vote)
원전	Agrawal, R. & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), 487–499.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
별칭	Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	SSL, semi-supervised machine learning, transductive learning, label-efficient learning	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
관련	5	4	5	5
요약	The Apriori algorithm, introduced by Agrawal and Srikant in 1994, is the foundational method for discovering frequent itemsets and association rules in transactional databases. It uses a breadth-first, level-wise search guided by the anti-monotone property of support to efficiently enumerate all item combinations that co-occur above a user-set minimum threshold, then extracts interpretable if-then rules from those patterns.	K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.	A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted.