পদ্ধতির তুলনা করুন

	Association Rules	FP-Growth (ফ্রিকোয়েন্ট প্যাটার্ন গ্রোথ)	কে-মিনস ক্লাস্টারিং
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning	Machine learning
উদ্ভবের বছর	1993	2000	1967 (formalized 1982)
প্রবর্তক	Agrawal, R., Imielinski, T., & Swami, A.	Jiawei Han, Jian Pei & Yiwen Yin	MacQueen, J. B.; Lloyd, S. P.
ধরন	Unsupervised pattern discovery	Frequent-itemset mining algorithm	Partitional clustering
মৌলিক উৎস	Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207–216.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
অপর নাম	market basket analysis, association rule mining, frequent itemset mining, affinity analysis	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
সম্পর্কিত	4	4	4
সারসংক্ষেপ	Association rule learning is an unsupervised technique that discovers co-occurrence patterns — 'if X then Y' implications — within large transactional datasets. Originally formalized by Agrawal, Imielinski, and Swami (1993) for supermarket basket analysis, it is now widely applied in e-commerce recommendation, health informatics, bioinformatics, and behavioral research.	FP-Growth, introduced by Jiawei Han, Jian Pei, and Yiwen Yin in 2000, mines frequent itemsets from transaction data without generating candidate sets, the costly step that slows the classic Apriori algorithm. It compresses the database into a frequent-pattern tree (FP-tree) in two scans, then grows frequent patterns recursively from that structure, making it dramatically faster than Apriori on large, dense datasets.	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.