An Enhanced Algorithm for Association Rule Mining in Huge Temporal Database

-The discovery of the association relationship among huge temporal database has been legendary to be helpful in selective selling, call analysis and business management. The current algorithms of association rule mining has limitations in handling temporal data in different data sets for these two main reasons. The first one lack of consideration of the exhibition period of each individual Item; the second reason is the lack of an equitable support counting basis for each item. The area of temporal data processing has much attention within the last decade as a result of the timerelated features of the information; one will extract abundant important data that cannot be extracted by the general methods of data mining. However Most of the data mining techniques perform to treat temporal data as an unordered collection of events, ignoring its temporal information. The work proposed in this paper applies knowledge discovery techniques on a series of huge datasets obtained over a partition that contains a large number of transactions in the consecutive time period, instead of applying on the whole database. In addition, Instead of extracting rules throughout the whole timeline, we will extract the rules for consecutive time intervals with different time granularities. The result for that will be developing more efficient approach for mining temporal association rules on large data sets.