AbstractA large class of problems deals with temporal data. Identifying temporal patterns in these datasets is a natural as well as an important task. In recent times, researchers have reported an algorithm for finding calendar-based periodic pattern in time-stamped data without considering the purchased quantities of the items. However, most of the real-life databases are nonbinary, and therefore, exploring various calendar-based patterns (yearly, monthly, weekly, daily) with their purchased quantities may discover information useful to improve the quality of business decisions. In this article, a technique is proposed to extract calendar-based periodic patterns from nonbinary transactions. In this connection, the concept of certainty factor has been introduced by incorporating transaction frequency for overlapped intervals. Algorithms have been designed to mine frequent itemsets along with intervals and quantity. In addition to that, we have designed an algorithm to find the periodicity of the pattern. The algorithm is tested with real-life data, and the results are given.
A New Algorithm for Mining Frequent Itemsets Based on Fp-Search Algorithm with K Road Pruning
