পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| Apriori Algorithm× | কে-মিনস ক্লাস্টারিং× | |
|---|---|---|
| ক্ষেত্র | যন্ত্র শিখন | যন্ত্র শিখন |
| পরিবার | Machine learning | Machine learning |
| উদ্ভবের বছর≠ | 1994 | 1967 (formalized 1982) |
| প্রবর্তক≠ | Agrawal, R. & Srikant, R. | MacQueen, J. B.; Lloyd, S. P. |
| ধরন≠ | Frequent itemset and association rule mining algorithm | Partitional clustering |
| মৌলিক উৎস≠ | 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. link ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ |
| অপর নাম | Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means |
| সম্পর্কিত≠ | 5 | 4 |
| সারসংক্ষেপ≠ | 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. |
| ScholarGateডেটাসেট ↗ |
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