Continuous query is widely recognized as a scheme for processing queries over data streams, and efficient methods for processing multiple continuous queries are needed. Our research group has proposed a multiple query optimization method for continuous queries. In our method, the system forms clusters of queries with similar execution patterns, and derives query plans sharing the result of common operators. Our previous experiments have shown that a parameter value in the clustering phase controls divisions of clusters and has a great impact on query processing effi- ciency. However, the optimal parameter value must be decided by trial and error. This paper extends our previous work. The proposed method automatically estimates the optimal value and iteratively adjusts it even if properties of underlying data streams dramatically change.