Document clustering using particle swarm optimization

By Xiaohui Cui, Thomas E. Potok, Paul J. Palathingal. Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005., 2005

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Music-Inspired Harmony Search Algorithm (2009, methodology): “These algorithms includes HS clustering [47], K-means (best known partitioning algorithm), genetic K-means (GA) [50], particle swarm optimization based clustering (PSO) [42] and a Mises-Fisher generative model based algorithm (GM) [51, 52].”
A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data (2013, methodology): “(2008) stated that the performance of HPSO using PSO and KM (Cui et al. 2005) could be improved for document clustering by transforming the high dimensional vector document matrix into a lower dimension matrix.”
Hybrid clustering analysis using improved krill herd algorithm (2018, methodology): “The PSO algorithm is applied to TC to generate a globally optimal solution [42].”
A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, neutral): “design [242], image clustering [243], document clustering [244], cluster analysis of web usage data [245] and network anomaly”
Ant-based and swarm-based clustering (2007, methodology): “…different from ant-based clustering, have also been applied for a number of different clustering tasks, most prominently spatial data mining (Macgill 2000; Openshaw and Macgill 1998; Folino and Spezzano 2002) and text mining (Cui and Potok 2005; Cui et al. 2005, 2006), as discussed in Sect.”