Mining Periodic Patterns from Non-binary Transactions
Jhimli Adhikari
Journal of Intelligent Computing, Vol. 9, No. 4, 2018.
DOI: https://doi.org/ 10.6025/jic/2018/9/4/144-156
Link: http://www.dline.info/jic/fulltext/v9n4/jicv9n4_2.pdf
Abstract:
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.