Periodicity Mining, “a Time Inference over High Utility Item Set Mining” – A Study
Arunkumar M. S1, Suresh P2, Gunavathi C3, Preethi S.4

1Arun Kumar M. S, Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamanglam (Tamil Nadu), India.
2Suresh P, Department of Information Technology, Kongu Engineering College, Erode (Tamil Nadu), India.
3Gunavathi C, School of Information Technology and Engineering, VIT University, Vellore (Tamil Nadu), India.
4Preethi S, Infinijth Technologies Pvt Ltd, Gobichettipalayam (Tamil Nadu), India.
Manuscript received on 10 December 2018 | Revised Manuscript received on 29 December 2018 | Manuscript Published on 09 January 2019 | PP: 59-65 | Volume-7 Issue-4S November 2018 | Retrieval Number: E1870017519/19©BEIESP
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Abstract: In the past few years, mining oftemporal frequent patterns from transactional database has gathered momentum. Numerous works and algorithms have been proposed for FIM [1,2,3,4], but the same models cannot be implemented to mine temporal patterns as none of the models are built to find patterns that consider periodicity of its occurrence in a database. The importance of an itemset really rests upon its utility rather than its participation count. Works over utility mining [5, 6 and 7] have gathered more momentum in this last decade and many research works have been carried out. In this paper, a survey is conducted on i) the works that led to periodic pattern mining, ii) the works over periodic pattern mining and iii) the extended and enhanced works of Periodic pattern mining.
Keywords: Temporal Mining, Utility Mining, Periodic Mining, High Utility Itemset Mining, Sequential Pattern Mining.
Scope of the Article: Data Mining