Research On The Key Technology Of Prediction And Association Rules Of University Emergencies Based On GEP And Complex Networks

University emergencies frequently occurred in the past few years,seriously impact on the schools themselves and the whole society. Miningthe potential association between the university emergencies predisposingfactors and result factors, it is great value to be effective in preventingfrom campus emergencies.However, traditional association rules mining algorithm needs toscan the database with several times in mining process, and produced alarge number of candidates set at the same time. Therefore, in connectionwith the set of campus emergencies, how to set up an efficient algorithmfor mining association rules, it is the current problems to be solved.In response to the shortcomings of traditional association rulemining algorithm, in this thesis, the GEP(Gene Expression Programming)and complex network will be introduced into association rule mining, andthe use of complex network to tectonic complex universities emergenciesmodel, combine with GEP simple genetic code and the ability of globallysearch for optimal solutions to optimize the original data for associationrules mining, in order to reduce the time of mining association rules, so asto make up for the lack of traditional algorithms. The main work of thisthesis as follow:Firstly,this thesis aims at the features of campus emergencies and theabstraction of the attributes of each emergency, according to event attributes, divide it into predisposing factors and result factors, and thenmake the data clean, Integrate data from different data sources into adatabase.Secondly, according to the campus emergencies characteristics,determine the nodes and edges of complex network, use complex networkmodel to construct universities emergencies.Thirdly, on this basis, introduce gene expression programmingalgorithm for global search optimal solution, propose communitypartitioning algorithm of GEP-ECD, divide complex network model ofemergency into communities and output each community table.Fourthly, propose GCAR algorithm for mining each local associationrule community table, take the Confidence and Support as the evaluationcriterion for mining the effective rules, these rules will provide the basisfor predicting campus emergencies.Fifthly, combine instance with using these useful rules to establishuniversities emergency prediction model, and the experiments show thatthis model achieve the better results.