An algebraic semigroup method for discovering maximal frequent itemsets

Article Computer Science, Artificial Intelligence

CL-MAX: a clustering-based approximation algorithm for mining maximal frequent itemsets

Seyed Mohsen Fatemi et al.

Summary: The paper introduces an approximation algorithm for frequent itemset mining by converting the problem into a clustering problem, which significantly improves the algorithm's efficiency. Experimental results show that the proposed algorithm is almost always faster than existing deterministic algorithms while retaining up to 95% accuracy.

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS (2021)

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Article Chemistry, Multidisciplinary

Multi-Objective Optimization for High-Dimensional Maximal Frequent Itemset Mining

Yalong Zhang et al.

Summary: A multi-objective optimization algorithm was proposed for mining frequent itemsets in high-dimensional data, aiming to search for the maximal frequent itemset and achieve individual resurgency through recording and eliminating lethal gene fragments. The set of Pareto optimal solutions for frequent itemsets was ultimately obtained.

APPLIED SCIENCES-BASEL (2021)

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Article Computer Science, Artificial Intelligence

On the Efficient Representation of Datasets as Graphs to Mine Maximal Frequent Itemsets

Zahid Halim et al.

Summary: This research introduces a graph-based approach to represent transactional databases, storing all information relevant to mining FIs in one pass, along with an algorithm for extracting FIs from this structure. Experimental results demonstrate that the proposed approach outperforms other methods in terms of time efficiency.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2021)

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Review Computer Science, Artificial Intelligence