Tremendous amount of data streams are often generated by dynamic environments such as stock’s and bond’s price indices, telecommunications data, audio and video data, Network traffic and data related to various Shopping malls. Mining regular patterns is one of the most important task in data mining. A time series database consists of various sequences of values that are obtained over a stipulated period of time. The values are typically measured at equal time stamps (eg., hourly, daily, weekly) which are sequence of ordered events, with or without concrete notations of time. The function is to mine all the transactional data which describes the behavior of various transactions. In an online business or in a shopping mall, the customers can purchase more than one item at a time. Frequent patterns are those that appear most often in a data set as a collection of various item sets or its subsequences. The algorithms like Apriori and FP Growth are used to mine the frequent patterns of a item set. The Apriori algorithm generates candidate set during its each iteration. It reduces the dataset by removing all the irregular itemsets which does not meet the minimum threshold values from the candidate sets. The most expensive phase of FP Growth algorithm is to generate a candidate set and to mine the database [1].