Abstract:
Pattern mining (PM) finds user-interested data patterns. Most studies have examined one pattern, like frequent or high-utility. Single-objective PM methods struggle to meet the growing needs of businesses in various industries.
This paper proposes a multi-objective problem model for high-quality pattern mining (HQPM) with support, occupancy, and utility. An improved multi-objective evolutionary algorithm for HQPM (MOEA-PM) solves the three-objective problem efficiently.
Two population initialization strategies ensure population distribution in the feasible solution space. An auxiliary tool is suggested to accelerate algorithm convergence based on model properties.
Experimental results on real-world datasets show that the proposed three-objective problem model with the MOEA-PM algorithm can find patterns that are frequent, useful, and relatively complete in transaction datasets. MOEA-PM outperforms state-of-the-art MOEA-based HQPM algorithms in efficiency, quality, and convergence.
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