UF-EVOLVE - UNCERTAIN FREQUENT PATTERN MINING

Shu Wang, Vincent Ng

2011

Abstract

Many frequent-pattern mining algorithms were designed to handle precise data, such as the FP-tree structure and the FP-growth algorithm. In data mining research, attention has been turned to mining frequent patterns in uncertain data recently. We want frequent-pattern mining algorithms for handling uncertain data. A common way to represent the uncertainty of a data item in record databases is to associate it with an existential probability. In this paper, we propose a novel uncertain-frequent-pattern discover structure, the mUF-tree, for storing summarized and uncertain information about frequent patterns. With the mUF-tree, the UF-Evolve algorithm can utilize the shuffling and merging techniques to generate iterative versions of it. Our main purpose is to discover new uncertain frequent patterns from iterative versions of the mUF-tree. Our preliminary performance study shows that the UF-Evolve algorithm is efficient and scalable for mining additional uncertain frequent patterns with different sizes of uncertain databases.

References

  1. Adnan, M., Alhajj, R., Barker, K., 2006. Constructing Complete FP-Tree for Incremental Mining of Frequent Patterns in Dynamic Databases. M. Ali and R. Dapoigny (Eds.): IEA/AIE 2006, LNAI 4031, pp. 363 - 372, 2006.
  2. Adnan, M., Alhajj, R., Barker, K., 2006. Constructing Complete FP-Tree for Incremental Mining of Frequent Patterns in Dynamic Databases. M. Ali and R. Dapoigny (Eds.): IEA/AIE 2006, LNAI 4031, pp. 363 - 372, 2006.
  3. Antova, L., Jansen, T., Koch, C., Olteanu, D., 2007. Fast and Simple Relational Processing of Uncertain Data. http://www.cs.cornell.edu/koch/www.infosys.unisb.de/publications/INFOSYS-TR-2007-2.pdf.
  4. Antova, L., Jansen, T., Koch, C., Olteanu, D., 2007. Fast and Simple Relational Processing of Uncertain Data. http://www.cs.cornell.edu/koch/www.infosys.unisb.de/publications/INFOSYS-TR-2007-2.pdf.
  5. Borgelt, C., 2005. An Implementation of the FP-growth Algorithm. OSDM'05, August 21, 2005, Chicago, Illinois, USA.
  6. Borgelt, C., 2005. An Implementation of the FP-growth Algorithm. OSDM'05, August 21, 2005, Chicago, Illinois, USA.
  7. Chau, M., Cheng, R., Kao, B., 2005. Uncertain Data Mining: A New Research Direction. In Proceedings of the Workshop on the Sciences of the Artificial, Hualien, Taiwan, December 7-8, 2005.
  8. Chau, M., Cheng, R., Kao, B., 2005. Uncertain Data Mining: A New Research Direction. In Proceedings of the Workshop on the Sciences of the Artificial, Hualien, Taiwan, December 7-8, 2005.
  9. Cheung, W., Zaiane, O. R., 2003. Incremental Mining of Frequent Patterns Without Candidate Generation or Support Constraint. Proceedings of the Seventh International Database Engineering and Applications Symposium (IDEAS'03).
  10. Cheung, W., Zaiane, O. R., 2003. Incremental Mining of Frequent Patterns Without Candidate Generation or Support Constraint. Proceedings of the Seventh International Database Engineering and Applications Symposium (IDEAS'03).
  11. Chui, C. K., Kao, B., Hung, E., 2007. Mining Frequent Itemsets from Uncertain Data. Z.-H. Zhou, H. Li, and Q. Yang (Eds.): PAKDD 2007, LNAI 4426, pp. 47- 58, 2007.
  12. Chui, C. K., Kao, B., Hung, E., 2007. Mining Frequent Itemsets from Uncertain Data. Z.-H. Zhou, H. Li, and Q. Yang (Eds.): PAKDD 2007, LNAI 4426, pp. 47- 58, 2007.
  13. Ezeife, C. I., Su, Y., 2002. Mining Incremental Association Rules with Generalized FP-Tree. R. Cohen and B. Spencer (Eds.): AI 2002, LNAI 2338, pp. 147-160, 2002.
  14. Ezeife, C. I., Su, Y., 2002. Mining Incremental Association Rules with Generalized FP-Tree. R. Cohen and B. Spencer (Eds.): AI 2002, LNAI 2338, pp. 147-160, 2002.
  15. Han, J., Pei, J., Yin, Y., Mao, R., 2004. Mining Frequent Patterns without Candidate Generation: A FrequentPattern Tree Approach. Data Mining and Knowledge Discovery, 8, 53-87, 2004.
  16. Han, J., Pei, J., Yin, Y., Mao, R., 2004. Mining Frequent Patterns without Candidate Generation: A FrequentPattern Tree Approach. Data Mining and Knowledge Discovery, 8, 53-87, 2004.
  17. Hong, T. P., Lin, C. W., Wu, Y. L., 2008. Incrementally fast updated frequent pattern trees. Expert Systems with Applications 34 (2008) 2424-2435.
  18. Hong, T. P., Lin, C. W., Wu, Y. L., 2008. Incrementally fast updated frequent pattern trees. Expert Systems with Applications 34 (2008) 2424-2435.
  19. Leung, C. K. S., Carmichael, C., Hao, B., 2007. Efficient Mining of Frequent Patterns from Uncertain Data. ICDM-DUNE 2007.
  20. Leung, C. K. S., Carmichael, C., Hao, B., 2007. Efficient Mining of Frequent Patterns from Uncertain Data. ICDM-DUNE 2007.
  21. Leung, C. K. S., Mateo, M. A. F., Brajczuk, D. A., 2008. A Tree-Based Approach for Frequent Pattern Mining from Uncertain Data. T. Washio et al. (Eds.): PAKDD 2008, LNAI 5012, pp. 653-661, 2008.
  22. Leung, C. K. S., Mateo, M. A. F., Brajczuk, D. A., 2008. A Tree-Based Approach for Frequent Pattern Mining from Uncertain Data. T. Washio et al. (Eds.): PAKDD 2008, LNAI 5012, pp. 653-661, 2008.
  23. Li, H. F., Lee, S. Y., Shan, M. K., 2004. An Efficient Algorithm for Mining Frequent Itemsets over the Entire History of Data Streams. http:// citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.81. 9955.
  24. Li, H. F., Lee, S. Y., Shan, M. K., 2004. An Efficient Algorithm for Mining Frequent Itemsets over the Entire History of Data Streams. http:// citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.81. 9955.
Download


Paper Citation


in Harvard Style

Wang S. and Ng V. (2011). UF-EVOLVE - UNCERTAIN FREQUENT PATTERN MINING . In Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-8425-53-9, pages 74-84. DOI: 10.5220/0003499400740084


in Harvard Style

Wang S. and Ng V. (2011). UF-EVOLVE - UNCERTAIN FREQUENT PATTERN MINING . In Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-8425-53-9, pages 74-84. DOI: 10.5220/0003499400740084


in Bibtex Style

@conference{iceis11,
author={Shu Wang and Vincent Ng},
title={UF-EVOLVE - UNCERTAIN FREQUENT PATTERN MINING},
booktitle={Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2011},
pages={74-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003499400740084},
isbn={978-989-8425-53-9},
}


in Bibtex Style

@conference{iceis11,
author={Shu Wang and Vincent Ng},
title={UF-EVOLVE - UNCERTAIN FREQUENT PATTERN MINING},
booktitle={Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2011},
pages={74-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003499400740084},
isbn={978-989-8425-53-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - UF-EVOLVE - UNCERTAIN FREQUENT PATTERN MINING
SN - 978-989-8425-53-9
AU - Wang S.
AU - Ng V.
PY - 2011
SP - 74
EP - 84
DO - 10.5220/0003499400740084


in EndNote Style

TY - CONF
JO - Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - UF-EVOLVE - UNCERTAIN FREQUENT PATTERN MINING
SN - 978-989-8425-53-9
AU - Wang S.
AU - Ng V.
PY - 2011
SP - 74
EP - 84
DO - 10.5220/0003499400740084