Top-K Strongly Related Projects Pairs Mining Algorithm Based on Threshold Estimate

Ming Ying Luo

doi:10.4028/www.scientific.net/AMR.268-270.1103

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 268-270Top-K Strongly Related Projects Pairs Mining...

Top-K Strongly Related Projects Pairs Mining Algorithm Based on Threshold Estimate

Abstract:

This paper presents a candidate set in the case does not produce only scan through a database to extract the top-k strongly correlated item pairs method. Secondly, the structural information using a relational database, an appropriate estimate of the effective threshold, so that the results of K contains at least one project to effectively address the relational database on the Top-K strongly related to the problem of mining projects, and without Pay too much computational cost.

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Periodical:

Advanced Materials Research (Volumes 268-270)

Pages:

1103-1109

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.268-270.1103

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Online since:

July 2011

Authors:

Ming Ying Luo

Keywords:

Projects Pairs, Threshold Estimate, Top-K

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References

[1] P. Songram, V. Boonjing, S. Intakosum. Closed multidimensional sequential pattern mining. In Proc. of the 3rd International Conference on Information Technology: New Generations, 2006: 512-517.

DOI: 10.1109/itng.2006.41

Google Scholar

[2] H. Xiong, P. -N. Tan, V. Kumar. Hyperclique pattern discovery. Data Mining and Knowledge Discovery, 2006, 13(2): 219-242.

DOI: 10.1007/s10618-006-0043-9

Google Scholar

[3] J. Cheng, Y. Ke, W. Ng. Graphgen. A graph synthetic generator. Available from: http: /www. cse. ust. hk/graphgen/. Srinivasan, S. Muggleton, R. King, et al. Mutagenesis: Ilp experiments in a nondeterminate biological domain. In Proc. of the 4th International Workshop on Inductive Logic Programming, 1994: 217-232.

Google Scholar

[4] Borgelt, M. R. Berthold. Mining molecular fragments: Finding relevant substructures of molecules. In Proc. of the 2002 IEEE International Conference on Data Mining, 2002: 51-58.

DOI: 10.1109/icdm.2002.1183885

Google Scholar

[5] Wynne Hsu, Mong Li Lee, Ji Zhang. Image mining: Trends and Developments. Journal of Intelligent Information Systems, 2002, 19(1): 7-23.

Google Scholar

[6] Kitamoto. Data mining for typhoon image collection. In Second International Workshop on Multimedia Data Mining (MDM/KDD'2001), 2001: 68-78.

Google Scholar

[7] Fayyad U.M., Djorgovski S.G., N Weir. Automating the analysis and cataloging of sky surveys. Advances in Knowledge Discovery and Data Mining, 1996: 471- 493.

Google Scholar

[8] Zaiane. Mining Multimedia Data. CASCON: Meeting of Minds, 1998: 76-87.

Google Scholar

[9] Ordonez C., Omiecinski E. Discovering association rules based on image content. In IEEE Advances in Digital Libraries Conference, 1999: 113-125.

DOI: 10.1109/adl.1999.777689

Google Scholar