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| Association Rule Mining (Apriori)× | K-Means Clustering× | |
|---|---|---|
| Fagområde | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 1994 | 1967 |
| Ophavsperson≠ | Rakesh Agrawal & Ramakrishnan Srikant | MacQueen, J. |
| Type≠ | Unsupervised pattern discovery algorithm | Partitional clustering (centroid-based) |
| Oprindelig kilde≠ | Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD, 207–216. DOI ↗ | 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. link ↗ |
| Aliasser | Market Basket Analysis, Frequent Itemset Mining, Birliktelik Kuralı Madenciliği, Itemset Association Analysis | K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering |
| Relaterede | 3 | 3 |
| Resumé≠ | 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. | 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. |
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