Association rule mining perhaps the most widely described technique among the minding paradigms. The temporal association rule mining in the association rule mining tries to find relations among items in datasets. The temporal association mining has strength in detecting the dynamic nature of databases. Unfortunately the current mining methods ignore the consideration of database content updates. In the current research we have introduced the Incremental Standing method for Segment Progressive Filter (ISPF). The proposed technique can support the database update and mine updated datasets. We prove that the proposed algorithm is an optimal way of mining. We have applied the scan reduction technique to generate all candidate k-item sets to form 2-candidate item sets directly. We also bring good experimentation to validate and document clearly the algorithm with good examples.