Most of the periodic pattern mining algorithms extract fully periodic patterns by strictly monitoring the cyclic behaviour of patterns in transactional as well as temporal databases. The most recent and preferred method for discarding non-periodic uninteresting patterns is partial periodic pattern mining, which has control over the strictness measure on cyclic repetitions of patterns. Recently, a variety of industries, including fraud detection, telecommunications, retail marketing, research, and medical have found applications for rare association rule mining, which uncovers unusual or unexpected combinations. A limited amount of literature demonstrated how periodicity is essential in mining low-support rare patterns. However, time of occurrence is also a vital phrase that is ignored which further aids in significant information retrieval. With this inspiration, a novel depth-first search framework named 3P-BitVectorMiner, is proposed to extract entire partial periodic patterns from a temporal database. Experiments are carried out by varying support and periodicity thresholds for a variety of datasets. It is found that 3P-BitVectorMiner consistently displays greater performance over the state-of-the-art algorithm 3P-Growth. Further, the scalability of the 3P-BitVectorMiner algorithm is also presented to demonstrate the efficiency over the 3P-Growth algorithm on large temporal databases. In addition, two variations named RFPP-BitVectorMiner and R3P-BitVectorMiner are proposed to mine rare fully periodic patterns and rare partial periodic patterns from temporal databases respectively. Different experiments carried out show that these proposed frameworks successfully capture periodic rare patterns in temporal databases.
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Materials Science(all)