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Efficient Mining Regularly Frequent Patterns in Transactional Databases

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Database Systems for Advanced Applications (DASFAA 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7238))

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Abstract

Finding interesting patterns plays an important role in several data mining applications, such as market basket analysis, medical data analysis, and others. The occurrence frequency of patterns has been regarded as an important criterion for measuring interestingness of a pattern in several applications. However, temporal regularity of patterns can be considered as another important measure for some applications. In this paper, we propose an efficient approach for miming regularly frequent patterns. As for temporal regularity measure, we use variance of interval time between pattern occurrences. To find regularly frequent patterns, we utilize pattern-growth approach according to user given min_support and max_variance threshold. Extensive performance study shows that our approach is time and memory efficient in finding regularly frequent patterns.

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References

  1. Agrawal, R., Imielinski, T., Swami, A.N.: Mining Association Rules Between Sets of Items in Large Databases. In: ACM SIGMOD International Conference on Management of Data, pp. 207–216 (1993)

    Google Scholar 

  2. Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation. In: ACM International Conference on Management of Data, pp. 1–12 (2000)

    Google Scholar 

  3. Tanbeer, S.K., Ahmed, C.F., Jeong, B.-S., Lee, Y.-K.: RP-Tree: A Tree Structure to Discover Regular Patterns in Transactional Database. In: Fyfe, C., Kim, D., Lee, S.-Y., Yin, H. (eds.) IDEAL 2008. LNCS, vol. 5326, pp. 193–200. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  4. Chi, Y., Wang, H., Yu, P.S., Muntz, R.R.: Catch the Moment: Maintaining Closed Frequent Itemsets Over a Data Stream Sliding Window. Knowledge and Information System 10(3), 265–294 (2006)

    Article  Google Scholar 

  5. Zaki, M.J., Hsiao, C.-J.: Efficient algorithms for mining closed itemsets and their lattice structure. IEEE Transactions on Knowledge and Data Engineering 17(4), 462–478 (2005)

    Article  Google Scholar 

  6. Huang, Y., Xiong, H., Wu, W., Deng, P., Zhang, Z.: Mining maximal hyperclique pattern: A hybrid serach stategy. Information Sciences 177, 703–721 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  7. Minh, Q.T., Oyanagi, S., Yamazaki, K.: Mining the K-Most Interesting Frequent Patterns Sequentially. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds.) IDEAL 2006. LNCS, vol. 4224, pp. 620–628. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  8. Wang, H., Perng, C.-S., Ma, S., Yu, P.S.: Demand-driven frequent itemset mining using pattern structures. Knowledge and Information Systems 8, 82–102 (2005)

    Article  Google Scholar 

  9. Tanbeer, S.K., Ahmed, C.F., Jeong, B.-S., Lee, Y.-K.: Efficient Single-Pass Frequent Pattern Mining using a prefix-tree. Information Sciences 179, 559–583 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  10. Tanbeer, S.K., Ahmed, C.F., Jeong, B.-S., Lee, Y.-K.: Discovering Periodic-Frequent Patterns in Transactional Databases. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS (LNAI), vol. 5476, pp. 242–253. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  11. Elfeky, M.G., Aref, W.G., Elmagarmid, A.K.: Periodicity detection in time series databases. IEEE Transactions on Knowledge and Data Engineering 17(7), 875–887 (2005)

    Article  Google Scholar 

  12. Han, J., Dong, G., Yin, Y.: Efficient mining of partial periodic patterns in time series database. In: Proceedings of 15th International Conference on Data Engineering, pp. 106–115 (1999)

    Google Scholar 

  13. Ozden, B., Ramaswamy, S., Silberschatz, A.: Cyclic Association Rules. In: 14th International Conference on Data Engineering, pp. 412–421 (1998)

    Google Scholar 

  14. Toroslu, I.H., Kantarcıoǧlu, M.: Mining Cyclically Repeated Patterns. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2001. LNCS, vol. 2114, pp. 83–92. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. of Computer Engineering, Kyung Hee University, 1 Seochun-dong, Kiheung-gu, Yongin-si, Kyunggi-do, 446-701, Republic of Korea

    Md. Mamunur Rashid, Md. Rezaul Karim & Byeong-Soo Jeong

  2. Computer Science Dept., Korea Advanced Institute of Science and Technology, 335 Guseong-dong, Yuseong-gu, Daejeon, 305-701, Republic of Korea

    Ho-Jin Choi

Authors
  1. Md. Mamunur Rashid
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  2. Md. Rezaul Karim
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  3. Byeong-Soo Jeong
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  4. Ho-Jin Choi
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Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, Seoul National University, Gwanak-ro, Gwanak-gu, 151747, Seoul, South Korea

    Sang-goo Lee

  2. Computer School, Wuhan University, Luo-jia-shan, Wuchang, 430081, Wuhan, Hubei Province, China

    Zhiyong Peng

  3. School of Information Technology and Electrical Engineering, University of Queensland, QLD 4072, Brisbane, Australia

    Xiaofang Zhou

  4. Department of Computer Science, Kangwon National University, 192-1, Hyoja2-Dong, Chuncheon, 200701, Kangwon, South Korea

    Yang-Sae Moon

  5. Institute for Computer Science and Business Information, University of Duisburg-Essen, Schützenbahn 70, 45117, Essen, Germany

    Rainer Unland

  6. School of Information and Communication Engineering, Chungbuk National University, 52 Naesudong-ro, Heungdeok-gu, Cheongju, 4072, Chungbuk, South Korea

    Jaesoo Yoo

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© 2012 Springer-Verlag Berlin Heidelberg

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Rashid, M.M., Karim, M.R., Jeong, BS., Choi, HJ. (2012). Efficient Mining Regularly Frequent Patterns in Transactional Databases. In: Lee, Sg., Peng, Z., Zhou, X., Moon, YS., Unland, R., Yoo, J. (eds) Database Systems for Advanced Applications. DASFAA 2012. Lecture Notes in Computer Science, vol 7238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29038-1_20

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  • DOI: https://doi.org/10.1007/978-3-642-29038-1_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29037-4

  • Online ISBN: 978-3-642-29038-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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