With the recent developments in computer storage and information technology, massive amounts of data have been stored and it is very difficult to process them without automatic data analysis methods. Large amount of knowledge exist within these datasets but hidden from the users. Data mining is the process of extraction of unknown knowledge from large amounts of data. Knowledge is a concept beyond data and information. Knowledge is finding patterns and hidden trends among data and information. Different types of rules can be discovered by the process of data mining, including association rules, dir=ltr It has been discovered recently that time dependent information is important in data mining. So temporal patterns or rules should be discovered from temporal data, since it can provide accurate information about an evolving business domain, rather than a static one that conventional data mining is dealing with. Time is considered in discussed mining methods in this thesis. There are many time aspects where can associate with rules. One of these aspects is time interval. A time interval associate with each association rule showing when that rule is valid. Thus, knowing time interval of a pattern, usefulness of extracted knowledge is increased. Another time aspect where can associate with rules is time period. A series of repeated occurrences of a certain type of event at regular intervals is described as a periodic event. In this thesis both time interval and time period factors is discussed in mining temporal association rules. We extend one of existing teqniques in order to create a flexible schema for expressing
