Process discovery usually analyses frequent behaviour in event logs to gain an intuitive understanding of processes. However, there are some effective infrequent behaviours that help to improve business processes in real life. Most existing studies either ignore them or treat them as harmful behaviours. To distinguish effective infrequent sequences from noisy activities, this paper proposes an algorithm to analyse the distribution states of activities and the strong trans- fer relationships between behaviours based on maximum proba- bility paths. The algorithm divides episodic traces into two cate- gories: harmful and useful episodes, namely noisy activities and effective sequences. First, using conditional probability entropy, the infrequent logs are pre-processed to remove individual noisy activi- ties that are extremely irregularly distributed in the traces. Effective sequences are then extracted from the logs based on the state trans- fer information of the activities. The algorithm is based on a PM4Py implementation and is validated using synthetic and real logs. From the results, the algorithm not only preserves the key structure of the modeland reduces noise activity, but also improves the quality ofthe model