Details

Autor(en) / Beteiligte

• Song, Wei
• Li, Junya

Titel

Discovering High Utility Itemsets Using Set-Based Particle Swarm Optimization

Ist Teil von

• Advanced Data Mining and Applications, p.38-53

Ort / Verlag

Cham: Springer International Publishing

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Mining high utility itemsets (HUIs) is a hot research topic in data mining. Algorithms based on evolutionary computation are attracting increasing attention because they have the advantage of avoiding the combinatorial explosion of the HUI search space. Among evolutionary methods used for mining HUIs, particle swarm optimization (PSO) is the most popular. Existing PSO-based HUI mining (HUIM) algorithms transform positions according to the result of applying the sigmoid function to the velocity. In this paper, we propose an HUIM algorithm based on set-based PSO (S-PSO) called HUIM-SPSO, which mainly considers elements in positions whose velocities are high. We introduce the modeling of HUIM using S-PSO, and explain HUIM-SPSO in detail. To reflect the diversity of the mining results, we propose the measure of the bit edit distance. Extensive experimental results show that the HUIM-SPSO algorithm is efficient and can discover more HUIs with a high degree of diversity.