Knowledge community of practice (CoP) is a popular approach for knowledge management implementation in construction organizations including contractors and A/E firms. In order to evaluate and improve the performance of the knowledge CoPs, quantification methods for performance measurement were proposed in previous researches. Profound implications may be inferred from the performance data recorded from daily knowledge management activities. Such implications provide directions of valuable strategies for administration schemes and system modifications. To achieve such goals, the performance improvement patterns and rules should be identified. In this paper MS SQL Server® was adopted to performed Data Ming (DM) tasks that dig out the abovementioned patterns and rules from the CoP performance data. Three DM techniques (Decision trees, Clusters, and Association Rules) were employed to mine the rules and patterns existing in the 4,892 historic performance data recorded from the CoPs of a leading A/E consulting firm in Taiwan. Performance improvement strategies are then inferred and planned based on the rules and patterns discovered.