পদ্ধতির তুলনা করুন

	সিদ্ধান্ত বৃক্ষ	FP-Growth (ফ্রিকোয়েন্ট প্যাটার্ন গ্রোথ)
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1984	2000
প্রবর্তক	Breiman, Friedman, Olshen & Stone	Jiawei Han, Jian Pei & Yiwen Yin
ধরন	Recursive partitioning (if-then rules)	Frequent-itemset mining algorithm
মৌলিক উৎস	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.
অপর নাম	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme
সম্পর্কিত	5	4
সারসংক্ষেপ	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.	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.