id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_r3mmyvnufnawhgtys6o7f7z7lq	Saood Iqbal	TKFIM: Top-K frequent itemset mining technique based on equivalence classes	2021	27	.pdf	application/pdf	11080	1546	77	the support threshold, called Top-k frequent itemsets mining (TKFIM). TKFIM: Top-K frequent itemset mining technique based on equivalence classes. of the transaction table for finding frequent itemsets that result in overhead on input and FIs. It refers to the user's choice of frequent itemsets in the dataset. Top-most frequent itemset mining technique that processes the top N impressive results Top-k algorithms based on FP-growth use FP-tree for pattern mining. (2002) proposed TFP (Top-k frequent closed itemsets mining algorithm), which user-specified support threshold parameter can affect the performance of the FIs mining itemsets of highest support, and it mines the candidates of the current class-based on the In the area of Frequent Itemset Mining, the very first algorithm, i.e., Apriori, was proposed based Top-k FIs techniques make use of FP-tree for frequent mining patterns. Data was taken from the Frequent Itemset Mining Dataset Repository (http: Mining frequent itemsets without support threshold: with	./cache/work_r3mmyvnufnawhgtys6o7f7z7lq.pdf	./txt/work_r3mmyvnufnawhgtys6o7f7z7lq.txt