Top-K Miner: top-K identical frequent itemsets discovery without user support threshold

Frequent itemsets (FIs) mining is a prime research area in association rule mining. The customary techniques find FIs or its variants on the basis of either support threshold value or by setting two generic parameters, i.e., N (topmost itemsets) and Kmax\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$K_\mathrm{{max}}$$\end{document} (size of the itemsets). However, users are unable to mine the absolute desired number of patterns because they tune these approaches with their approximate parameters settings. We proposed a novel technique, top-K Miner that does not require setting of support threshold, N and Kmax\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$K_\mathrm{{max}}$$\end{document} values. Top-K Miner requires the user to specify only a single parameter, i.e., K to find the desired number of frequent patterns called identical frequent itemsets (IFIs). Top-K Miner uses a novel candidate production algorithm called join-FI algorithm. This algorithm uses frequent 2-itemsets to yield one or more candidate itemsets of arbitrary size. The join-FI algorithm follows bottom-up recursive technique to construct candidate-itemsets-search tree. Finally, the generated candidate itemsets are manipulated by the Maintain-Top-K_List algorithm to produce Top-K_List of the IFIs. The proposed top-K Miner algorithm significantly outperforms the generic benchmark techniques even when they are running with the ideal parameters settings.