Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11779/2355
Title: Quantum Fp-Growth for Association Rules Mining
Authors: Belkadi, Widad Hassina
Drias, Yassine
Drias, Habiba
Keywords: Quantum machine learning
Frequent itemset mining
Association rules mining
Fp-growth
Ibm qasm simulator
Qiskit
Publisher: Springer international Publishing Ag
Series/Report no.: Information Systems Engineering and Management
Abstract: Quantum computing, based on quantum mechanics, promises revolutionary computational power by exploiting quantum states. It provides significant advantages over classical computing regarding time complexity, enabling faster and more efficient problem-solving. This paper explores the application of quantum computing in frequent itemset mining and association rules mining, a crucial task in data mining and pattern recognition. We propose a novel algorithm called Quantum FP-Growth (QFP-Growth) for mining frequent itemsets. The QFP-Growth algorithm follows the traditional FP-Growth approach, constructing a QF-list, then the QFP-tree, a quantum radix tree, to efficiently mine frequent itemsets from large datasets. We present a detailed analysis of each step in the QFP-Growth algorithm, providing insights into its time complexity and computational efficiency. Our algorithm outperforms classical FP-Growth with a quadratic improvement in error dependence, showcasing the power of quantum algorithms in data mining. To validate the effectiveness of our approach, we conducted experiments using the IBM QASM simulator, qiskit. The results demonstrate the efficiency and effectiveness of our QFP-Growth algorithm in mining frequent itemsets from a transactional database.
URI: https://doi.org/10.1007/978-3-031-59318-5_8
https://hdl.handle.net/20.500.11779/2355
ISBN: 9783031602740
9783031593185
9783031593178
ISSN: 3004-958X
Appears in Collections:WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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