01550nas a2200205   4500008004100000245006200041210006200103260000900165300001400174490000600188520089100194653002201085653003101107653003101138100001401169700001601183700001501199700001301214856011701227       2011                        eng d00aRole of segment progressive filter in dynamic data mining0 aRole of segment progressive filter in dynamic data mining  c2011  a171 - 1750 v93 aAssociation 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.10aMining algorithms10aSegment Progressive Filter10aTemporal association rules1 aNaqvi, M.1 aHussain, K.1 aAsghar, S.1 aFong, S.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-80053099423&partnerID=40&md5=e113d860dc0339efdd1f9af97d9c9121