As the volume of network traffic increases due to the proliferation of distributed systems and the growth of real-time applications, a good understanding of traffic distribution and patterns becomes critical in network control and performance management. In this work, we upgrade the facilities of network management from traditional file systems to database and knowledge base systems and apply machine learning techniques to discover traffic patterns which are difficult to discern by human operators among a large volume of measurements. An experiment on intercdnnected LANs is conducted where some interesting patterns are found. The results show a strong traffic locality and some cyclic traffic patterns. The discovered rule base can describe the traffic distribution and patterns which need to be captured for any sophisticated performance management. The experiment has shown the high applicability of induction techniques to network management.