Quantum Algorithm for Mining Frequent Patterns for Association Rule Mining

Maximum frequent pattern generation from a large database of transactions and items for association rule mining is an important research topic in data mining. Association aims to discover interesting correlations, patterns, associations, or causal structures between hidden database. By exploiting quantum computing, we propose efficient search algorithm design the maximum patterns. We modified Grover’s so that subspace arbitrary symmetric states used instead whole space. presented novel oracle employs counter count comparator check with minimum support threshold. The proposed derived increases rate correct solutions since only subspace. Furthermore, our significantly scales optimizes required number qubits design, which directly reflected positively on performance. Our can accommodate more still have good performance small qubits.