Journal of Intelligent Computing Jhimli Adhikari 9 4 2018 https://doi.org/ 10.6025/jic/2018/9/4/144-156 http://www.dline.info/jic/fulltext/v9n4/jicv9n4_2.pdf Pattern with time period is more valuable because it can better describe objective knowledge. Previous studies on periodic patterns from market basket data focus on patterns without considering the items with their purchased quantities. But in real-life transactions, an item could be purchased multiple times in a transaction and different items may have different quantity in the transactions. To solve this problem, we incorporate the concept of transaction frequency (TF) and database frequency (DF) of an item in a time interval. Our algorithm works in two phases. In first phase we mined locally frequent item sets along with the set of intervals and their database frequency range and second phase mines the two types of periodic patterns (cyclic and acyclic) from the list of intervals. Experimental results are provided to validate the study.