Vertaile menetelmiä

	Assosiaatiosääntöjen louhinta (Apriori)	Formaali konseptianalyysi (FCA)	K-Means-klusterointi
Tieteenala	Koneoppiminen	Pehmeä laskenta	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning	Machine learning
Syntyvuosi	1994	1982	1967
Kehittäjä	Rakesh Agrawal & Ramakrishnan Srikant	Rudolf Wille & Bernhard Ganter	MacQueen, J.
Tyyppi	Unsupervised pattern discovery algorithm	Lattice-based knowledge representation / concept mining	Partitional clustering (centroid-based)
Alkuperäislähde	Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD, 207–216.	Wille, R. (1982). Restructuring lattice theory: an approach based on hierarchies of concepts. In I. Rival (Ed.), Ordered Sets (pp. 445–470). Reidel.	MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.
Rinnakkaisnimet	Market Basket Analysis, Frequent Itemset Mining, Birliktelik Kuralı Madenciliği, Itemset Association Analysis	FCA, concept lattice analysis, Galois lattice, biçimsel kavram analizi	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
Liittyvät	3	3	3
Tiivistelmä	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.	Formal concept analysis derives a hierarchy of concepts from a simple table of which objects have which attributes. Founded by Rudolf Wille in 1982 on lattice theory, it pairs each set of objects with the attributes they all share to form 'formal concepts', then organizes these into a concept lattice — a mathematically grounded, interpretable hierarchy used for knowledge discovery, ontology building, and explainable analysis of categorical data.	K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis.