Abstract
Finding the associations among the itemsets and discovering the unknown or unexpected behavior are the major tasks of rare pattern mining. The support measure has the main contribution during the discovery of low support patterns. As the association of low support patterns may generate a bundle of spurious patterns, other measures are used to find the correlation between the itemsets. A generalization of frequent pattern mining called periodic frequent pattern mining (PFPM) is emerged as a promising field, focusing on the occurrence behavior of frequent patterns. On the contrary, the shape of occurrence in the case of rare pattern mining is not much studied. In this paper, a single scan algorithm called \( PRCPMiner\) is proposed to study the shape of occurrence of rare patterns. The proposed algorithm discovers periodic rare correlated patterns using different thresholds with respect to support, bond, and periodicity measures. The research shows the influence of these thresholds on the runtime performance for various datasets.
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References
Tanbeer, S. K., Ahmed, C. F., Jeong, B.-S., & Lee, Y.-K. (2008). Mining regular patterns in transactional databases. IEICE Transactions on Information and Systems, 91(11), 2568–2577.
Tanbeer, S. K., Ahmed, C. F., & Jeong, B.-S. (2010). Mining regular patterns in data streams. In International conference on database systems for advanced applications (pp. 399–413). Springer.
Tanbeer, S. K., Hassan, M. M., Almogren, A., Zuair, M., & Jeong, B.-S. (2017). Scalable regular pattern mining in evolving body sensor data. Future Generation Computer Systems, 75, 172–186.
Rashid, M. M., Karim, M. R., Jeong, B.-S., & Choi, H. J. (2012). Efficient mining regularly frequent patterns in transactional databases. In International conference on database systems for advanced applications (pp. 258–271). Springer.
Rashid, M. M., Gondal, I., & Kamruzzaman, J. (2013). Regularly frequent patterns mining from sensor data stream. In International conference on neural information processing (pp. 417–424). Springer.
Fournier-Viger, P., Yang, P., Li, Z., Chun-Wei Lin, J., & Kiran, R. U. (2020). Discovering rare correlated periodic patterns in multiple sequences. Data & Knowledge Engineering, 126, 101733.
Fournier-Viger, P., Lin, C.-W., Duong, Q.-H., Dam, T.-L., Ševčík, L., Uhrin, D., & Voznak, M. (2017). PFPM: Discovering periodic frequent patterns with novel periodicity measures. In Proceedings of the 2nd Czech-China scientific conference 2016. IntechOpen.
Fournier-Viger, P., Chun-Wei Lin, J., Duong, Q.-H., & Dam, T.-L. (2016). PHM: Mining periodic high-utility itemsets. In Industrial conference on data mining (pp. 64–79). Springer.
Bouasker, S., & Yahia, S. B. (2015). Key correlation mining by simultaneous monotone and anti-monotone constraints checking. In Proceedings of the 30th annual ACM symposium on applied computing (pp. 851–856).
Aryabarzan, N., Minaei-Bidgoli, B., & Teshnehlab, M. (2018). Negfin: An efficient algorithm for fast mining frequent itemsets. Expert Systems with Applications, 105, 129–143.
Bouasker, S., Hamrouni, T., & Yahia, S. B. (2012). New exact concise representation of rare correlated patterns: Application to intrusion detection. In Pacific-Asia conference on knowledge discovery and data mining (pp. 61–72). Springer.
Fournier-Viger, P., Chun-Wei Lin, J., Dinh, T., & Bac Le, H. (2016). Mining correlated high-utility itemsets using the bond measure. In International conference on hybrid artificial intelligence systems (pp. 53–65). Springer.
Uday Kiran, R., & Kitsuregawa, M. (2014). Novel techniques to reduce search space in periodic-frequent pattern mining. In International conference on database systems for advanced applications (pp. 377–391). Springer.
Uday Kiran, R., Kitsuregawa, M., & Krishna Reddy, P. (2016). Efficient discovery of periodic-frequent patterns in very large databases. Journal of Systems and Software, 112, 110–121.
Venkatesh, J. N., Uday Kiran, R., Krishna Reddy, P., & Kitsuregawa, M. (2018). Discovering periodic-correlated patterns in temporal databases. In Transactions on large-scale data and knowledge-centered systems XXXVIII (pp. 146–172). Springer.
Fournier-Viger, P., Yang, P., Chun-Wei Lin, J., & Kiran, R. U. (2019). Discovering stable periodic-frequent patterns in transactional data. In International conference on industrial, engineering and other applications of applied intelligent systems (pp. 230–244). Springer.
Amphawan, K., Lenca, P., Jitpattanakul, A., & Surarerks, A. (2016). Mining high utility itemsets with regular occurrence. Journal of ICT Research & Applications, 10(2).
Laoviboon, S., & Amphawan, K. (2017). Mining high-utility itemsets with irregular occurrence. In 2017 9th international conference on knowledge and smart technology (KST) (pp. 89–94). IEEE.
Szathmary, L., Valtchev, P., & Napoli, A. (2010). Generating rare association rules using the minimal rare itemsets family.
Adda, M., Wu, L., White, S., & Feng, Y. (2012). Pattern detection with rare item-set mining. arXiv:1209.3089.
Troiano, L., Scibelli, G., & Birtolo, C. (2009). A fast algorithm for mining rare itemsets. In 2009 ninth international conference on intelligent systems design and applications (pp. 1149–1155). IEEE.
Tsang, S., Koh, Y. S., & Dobbie, G. (2011). Rp-tree: Rare pattern tree mining. In International conference on data warehousing and knowledge discovery (pp. 277–288). Springer.
Borah, A., & Nath, B. (2017). Mining rare patterns using hyper-linked data structure (pp. 467–472).
Lu, Y., Richter, F., & Seidl, T. (2020). Efficient infrequent pattern mining using negative itemset tree. In Complex pattern mining (pp. 1–16). Springer.
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Jyothi, U.K., Rao, B.D., Geetha, M., Vora, H.K. (2023). Discovery of Periodic Rare Correlated Patterns from Static Database. In: Pati, B., Panigrahi, C.R., Mohapatra, P., Li, KC. (eds) Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering. Lecture Notes in Networks and Systems, vol 428. Springer, Singapore. https://doi.org/10.1007/978-981-19-2225-1_56
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DOI: https://doi.org/10.1007/978-981-19-2225-1_56
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