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Frequent Pattern Mining Often Produces An Enormous Number Of Frequent Patterns, Which Imposes A Great Challenge On Visualizing, Understanding And Further Analysis Of The Generated Patterns. This Calls For Finding A Small Number Of Representative Patterns To Best Approximate All Other Patterns. In This Paper, We Develop An Algorithm Called MinRPset To Find A Minimum Representative Pattern Set With Error Guarantee. MinRPset Produces The Smallest Solution That We Can Possibly Have In Practice Under The Given Problem Setting, And It Takes A Reasonable Amount Of Time To Finish When The Number Of Frequent Closed Patterns Is Below One Million. MinRPset Is Very Space-consuming And Time-consuming On Some Dense Datasets When The Number Of Frequent Closed Patterns Is Large. To Solve This Problem, We Propose Another Algorithm Called FlexRPset, Which Provides One Extra Parameter K To Allow Users To Make A Trade-off Between Result Size And Efficiency. We Adopt An Incremental Approach To Let The Users Make The Trade-off Conveniently. Our Experiment Results Show That MinRPset And FlexRPset Produce Fewer Representative Patterns Than RPlocal-an Efficient Algorithm That Is Developed For Solving The Same Problem.