Complex analytical environment is challenging environment for finding customer profiles. In situation where predictive model exists like Bayesian networks challenge became even bigger regarding combinatory explosion. Complex analytical environment can be caused by multiple modality of output variable, fact that each node of Bayesian network can potetnitaly be target variable for profiling, as well as from big data environment, which cause data complexity in way of data quantity. As an illustration of presented concept particle swarm optimization algorithm will be used as a tool, which will find profiles from developed predictive model of Bayesian network. This paper will show how partical swarm optimization algorithm can be powerfull tool for finding optimal customer profiles given target conditions as evidences within Bayesian networks.
Learning Bayesian networks based on bi-velocity discrete particle swarm optimization with mutation operator
