Abstract：Since most data attributes in data mining are redundant and do not have equal importance,it is not conductive to make a concise decision in data analysis.However,attribute reduction of the decision table is an important step in the rule extraction and data mining.Therefore,an improved optimization algorithm of tabu discrete particle pwarm optimization.The condition attribute set of decision table is used as the discrete particle swarm,and the tabu search algorithm is introduced as a local search strategy,thus enriching the diversity of particle swarm and enhancing the ability to seek global optimal solution.Without affecting the quality of classification,the condition attribute set is minimized through the interaction of particles,thereby deleting redundant attributes and simplifying the knowledge base.Finally,through examining multiple sets of data,a comparison experiment with other algorithms is made.As is shown,the algorithm is effective for attribute reduction.
2017, Vol. 38 
