Abstract
Periodic frequent pattern mining is an important data mining task for various decision making. However, it often presents a large number of periodic frequent patterns, most of which are not useful as their periodicities are due to random occurrence of uncorrelated items. Such periodic frequent patterns would most often be detrimental in decision making where correlations between the items of periodic frequent patterns are vital. To enable mine the periodic frequent patterns with correlated items, we employ a correlation test on periodic frequent patterns and introduce the productive periodic frequent patterns as the set of periodic frequent patterns with correlated items. We finally develop the productive periodic frequent pattern (PPFP) framework for mining our introduced productive periodic frequent patterns. PPFP is efficient and the productiveness measure removes the periodic frequent patterns with uncorrelated items.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Agrawal R, Srikant R (1995) Mining sequential patterns. In: 11th IEEE international conference on data engineering, IEEE pp 3–14
Elfeky MG, Aref WG, Elmagarmid AK (2005) Periodicity detection in time series databases. IIEEE Trans Knowl Data Eng 17(7):875–887
Fournier-Viger P, Gomariz A, Gueniche T, Soltani A, Wu C, Tseng VS (2014) SPMF: a java open-source pattern mining library. J Mach Learn Res 15:3389–3393
Kiran RU, Reddy PK (2010) Towards efficient mining of periodic-frequent patterns in transactional databases. In: Bringas PG, Hameurlain A, Quirchmayr G (eds) DASFAA 2010. LNCS, Springer, Berlin, pp 194–208
Kiran RU, Kitsuregawa M (2014) Novel techniques to reduce search space in periodic-frequent pattern mining. In: Bhowmick SS, Dyreson CE, Jensen CS, Lee ML, Muliantara A, Thalheim B (eds) DASFAA 2014. LNCS, Springer International Publishing, Berlin, pp 377–391
Kiran RU, Kitsuregawa M (2013) Discovering quasi-periodic-frequent patterns in transactional databases. In: Bhatnagar V, Srinivasa S (eds) BDA 2013. LNCS, Springer International Publishing, Heidelberg, pp 97–115
Kiran RU, Reddy PK (2011) An alternative interestingness measure for mining periodic-frequent patterns. In: Yu JX, Kim MH, Unland R (eds) DASFAA 2011. LNCS, Springer, Heidelberg, pp 183–192
Kumar V, Valli Kumari V (2013) Incremental mining for regular frequent patterns in vertical format. Int J Eng Technol 5(2):1506–1511
Ma S, Hellerstein JL (2001) Mining partially periodic event patterns with unknown periods. In: 17th IEEE international conference on data engineering, Heidelberg, pp 205–214
Rashid MM, Karim MR, Jeong BS, Choi HJ (2012) Efficient mining regularly frequent patterns in transactional databases. In: Lee S, Peng Z, Zhou X, Moon Y, Unland R, Yoo J (eds) DASFAA 2012. LNCS, Springer, Heidelberg, pp 258–271
Rashid MM, Gondal I, Kamruzzaman J (2013) Regularly frequent patterns mining from sensor data stream. In: Lee M, Hirose A, Hou ZG, Kil R (eds) NIP 2013. LNCS, Springer, Berlin, pp 417–424
Surana A, Kiran RU, Reddy PK (2012) An efficient approach to mine periodic-frequent patterns in transactional databases. In: Cao L, Huang JZ, Bailey J, Koh YS, Luo J (eds) PAKDD 2011 Workshops. LNAI, Springer, Heidelberg, pp 254–266
Tanbeer SK, Ahmed CF, Jeong BS, Lee YK (2009) Discovering periodic-frequent patterns in transactional databases. In: Theeramunkong T, Kijsirikul B, Cercone N, Ho T (eds) PAKDD 2009. LNAI, Springer, Heidelberg, pp 242–253
Webb GI (2010) Self-sufficient itemsets: an approach to screening potentially interesting associations between items. ACM Trans Knowl Discov Data 4(1):3:1–3:20
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Nofong, V.M. Discovering Productive Periodic Frequent Patterns in Transactional Databases. Ann. Data. Sci. 3, 235–249 (2016). https://doi.org/10.1007/s40745-016-0078-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40745-016-0078-8