One type of the partial periodic pattern is known as recurring patterns, which exhibit cyclic repetitions only for particular time period within a series. A key property of the patterns is the event can start, stop, and restart at anytime within a series. Therefore, the extracted meaningful knowledge from the patterns is challenging because the information can vary across patterns. The mining technique in recurring patterns plays an important role for discovering knowledge pertaining to seasonal or temporal associations between events. Most existing researches focus on discovering the recurring patterns in transaction. However, these researches for mining recurring patterns cannot discover recurring events across multiple transactions (inter-transaction) which often appears in many real-world applications such as the stock exchange market, social network, etc. In this study, the proposed algorithm, namely, CP-growth can efficiently perform in discovering the recurring patterns within inter-transaction. Besides, an efficient pruning technique to reduce the computational cost of discovering recurring patterns is developed in CP-growth algorithm. Experimental results show that recurring patterns can be useful in multiple transactions and the proposed algorithm, namely, CP-growth is efficient.

1. [1] T.-C. Fu, “A review on time series data mining,” Engineering Applications of Artificial Intelligence, Vol.24, Issue 1, pp. 164-181, 2011.
2. [2] P. Esling and C. Agon, “Time-series data mining,” ACM Comput. Surv., Vol.45, Issue 1, pp. 1-34, 2012.
3. [3] R. Pruengkarn, K. W. Wong, and C. C. Fung, “A Review of Data Mining Techniques and Applications,” J. Adv. Comput. Intell. Intell. Inform., Vol.21, No.1, pp. 31-48, 2017.
4. [4] H. Song and G. Li, “Tourism demand modelling and forecasting – A review of recent research,” Tourism Management, Vol.29, Issue 2, pp. 203-220, 2008.
5. [5] H. T. P. Thanh and A. P. Meesad, “Stock Market Trend Prediction Based on Text Mining of Corporate Web and Time Series Data,” J. Adv. Comput. Intell. Intell. Inform., Vol.18, No.1, pp. 22-31, 2014.
6. [6] T.-H. Lin, N. Kaminski, and Z. Bar-Joseph, “Alignment and classification of time series gene expression in clinical studies,” Bioinformatics, Vol.24, pp. i147-i155, 2008.
7. [7] H. S. Burkom, S. P. Murphy, and G. Shmueli, “Automated time series forecasting for biosurveillance,” Statistics in Medicine, Vol.26, Issue 22, pp. 4202-4218, 2007.
8. [8] R. Ouyang, L. Ren, W. Cheng, and C. Zhou, “Similarity search and pattern discovery in Hydrological time series data mining,” Hydrol. Process, Vol.24, Issue 9, pp. 1198-1210, 2010.
9. [9] N. Gadiraju, et al., “Periodic Pattern Mining-Algorithms and Applications,” Global J. of Computer Science and Technology, Vol.13, Issue 13, 2013.
10. [10] Z. Li, B. Ding, J. Han, R. Kays, and P. Nye, “Mining periodic behaviors for moving objects,” The Proc. of the 16th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Washington, DC, USA, 2010.
11. [11] J. Han, G. Dong, and Y. Yin, “Efficient mining of partial periodic patterns in time series database,” Proc. 15th Int. Conf. on Data Engineering, pp. 106-115, 1999.
12. [12] S. K. Tanbeer, C. F. Ahmed, B.-S. Jeong, and Y.-K. Lee, “Discovering Periodic-Frequent Patterns in Transactional Databases,” Advances in Knowledge Discovery and Data Mining, Berlin, Heidelberg, pp. 242-253, 2009.
13. [13] S. Ma and J. L. Hellerstein, “Mining partially periodic event patterns with unknown periods,” Proc. 17th Int. Conf. on Data Engineering, pp. 205-214, 2001.
14. [14] R. U. Kiran, H. Shang, M. Toyoda, and M. Kitsuregawa, “Discovering Recurring Patterns in Time Series,” Proc. 18th Int. Conf. on Extending Database Technology (EDBT), pp. 97-108, 2015.
15. [15] J. Han, W. Gong, and Y. Yin, “Mining Segment-Wise Periodic Patterns in Time-Related Databases,” Proc. Int. Conf. on Knowledge Discovery and Data Mining, pp. 214-218, 1998.
16. [16] C. Berberidis, I. Vlahavas, W. G. Aref, M. Atallah, and A. K. Elmagarmid, “On the Discovery of Weak Periodicities in Large Time Series,” Proc. of the 6th European Conf. on Principles of Data Mining and Knowledge Discovery, pp. 51-61, 2002.
17. [17] H. Cao, D. W. Cheung, and N. Mamoulis, “Discovering Partial Periodic Patterns in Discrete Data Sequences,” Pacific-Asia Conf. on Knowledge Discovery and Data Mining, Berlin, Heidelberg, pp. 653-658, 2004.
18. [18] R. Yang, W. Wang, and P. S. Yu, “InfoMiner+: mining partial periodic patterns with gap penalties,” Proc. IEEE Int. Conf. on Data Mining 2002, pp. 725-728, 2002.
19. [19] K. Amphawan, P. Lenca, and A. Surarerks, “Mining Top-K Periodic-Frequent Pattern from Transactional Databases without Support Threshold,” Int. Conf. on Advances in Information Technology, Berlin, Heidelberg, pp. 18-29, 2009.
20. [20] A. Surana, R. U. Kiran, and P. K. Reddy, “An efficient approach to mine periodic-frequent patterns in transactional databases,” Proc. of the 15th Int. Conf. on New Frontiers in Applied Data Mining, Shenzhen, China, pp. 254-266, 2012.
21. [21] R. U. Kiran and M. Kitsuregawa, “Novel Techniques to Reduce Search Space in Periodic-Frequent Pattern Mining,” Int. Conf. on Database Systems for Advanced Applications, Cham, pp. 377-391, 2014.
22. [22] R. U. Kiran, M. Kitsuregawa, and P. K. Reddy, “Efficient discovery of periodic-frequent patterns in very large databases,” J. of Systems and Software, Vol.112, pp. 110-121, 2016.
23. [23] M. M. Rashid, M. R. Karim, B.-S. Jeong, and H.-J. Choi, “Efficient Mining Regularly Frequent Patterns in Transactional Databases,” Int. Conf. on Database Systems for Advanced Applications, Vol.7238, pp. 258-271, 2012.
24. [24] R. U. Kiran and P. K. Reddy, “Towards Efficient Mining of Periodic-Frequent Patterns in Transactional Databases,” Int. Conf. on Database and Expert Systems Applications, Berlin, Heidelberg, pp. 194-208, 2010.
25. [25] J. N. Venkatesh, R. U. Kiran, P. K. Reddy, and M. Kitsuregawa, “Discovering Periodic-Frequent Patterns in Transactional Databases Using All-Confidence and Periodic-All-Confidence,” Int. Conf. on Database and Expert Systems Applications, Cham, pp. 55-70, 2016.
26. [26] T. Oates, “PERUSE: An unsupervised algorithm for finding recurring patterns in time series,” IEEE Int. Conf. on Data Mining 2002, pp. 330-337, 2002.
27. [27] Y. Mohammad and T. Nishida, “Approximately Recurring Motif Discovery Using Shift Density Estimation,” Int. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems, Berlin, Heidelberg, pp. 141-150, 2013.
28. [28] Y. Mohammad and T. Nishida, “Shift density estimation based approximately recurring motif discovery,” Applied Intelligence, Vol.42, Issue 1, pp. 112-134, 2015.
29. [29] J. Yang, W. Wang, and P. S. Yu, “Mining asynchronous periodic patterns in time series data,” IEEE Trans. on Knowledge and Data Engineering, Vol.15, pp. 613-628, 2003.
30. [30] R. Suwanwiwat, S. Kamonsantiroj, and L. Pipanmaekaporn, “Mining inter-transaction recurring patterns in time series,” 2016 IEEE Int. Conf. on Knowledge Engineering and Applications (ICKEA), pp. 23-28, 2016.
31. [31] A. K. H. Tung, H. Lu, J. Han, and L. Feng, “Efficient mining of intertransaction association rules,” IEEE Trans. on Knowledge and Data Engineering, Vol.15, Issue 1, pp. 43-56, 2003.
32. [32] R. Agrawal, T. Imieliński, and A. Swami, “Mining association rules between sets of items in large databases,” SIGMOD Int. Conf. on Management of Data, Vol.22, pp. 207-216, 1993.