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Incremental sequential pattern mining algorithms of Web site access in grid structure database

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Abstract

Sequential pattern mining is a process of knowledge discovery which finds the frequent subsequence as a mode from the sequence database. Web log database is typically dynamic. Web log records are generated constantly, and user access patterns will change accordingly. This study focused on taking advantage of the dynamic characteristics of the Web access database, delivering a fast and efficient incremental mining algorithm. An IncWTP algorithm suitable for Web access sequence mode is developed to handle non-simple path with dynamic data storage structure, and detect and delete the failed sequence data timely.

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References

  1. Shi H et al (2012) Association between PPAR-γ and RXR-α gene polymorphism and metabolic syndrome risk: a case-control study of a Chinese han population. Arch Med Res 43(3):233–242

    Article  MathSciNet  Google Scholar 

  2. Lee CKM, Lam JSL (2012) Managing reverse logistics to enhance sustainability of industrial marketing. Ind Mark Manage 41(4):589–598

    Article  Google Scholar 

  3. Zeiler A, Faltermeier R (2013) Weighted sliding empirical mode decomposition for online analysis of biomedical time series. Neural Process Lett 37(1):21–32

    Article  Google Scholar 

  4. Aras N, Aksen D, Gönül A (2008) Tanuğur, Locating collection centers for incentive-dependent returns under a pick-up policy with capacitated vehicles. Eur J Oper Res 191(3):1223–1240

    Article  MATH  Google Scholar 

  5. Alagador D, Cerdeira JO (2007) Designing spatially-explicit reserve networks in the presence of mandatory sites. Biol Conserv 137(2):254–262

    Article  Google Scholar 

  6. Brandão SN, Sauer J, Schön I (2010) Circumantarctic distribution in Southern Ocean benthos? A genetic test using the genus Macroscapha (Crustacea, Ostracoda) as a model. Mol Phylogenet Evol 55(3):1055–1069

    Article  Google Scholar 

  7. Kerh T, Gunaratnam D, Chan YL (2010) Neural computing with genetic algorithm in evaluating potentially hazardous metropolitan areas result from earthquake. Neural Comput Appl 19(4):521–529

    Article  Google Scholar 

  8. Timotheou Stelios (2011) A novel weight initialization method for the random neural network. Neurocomputing 56(1):345–363

    Google Scholar 

  9. Dash NB et al (2010) Hybrid neural modeling for groundwater level prediction. Neural Comput Appl 19(8):1251–1263

    Article  Google Scholar 

  10. Dehuri S, Cho SB (2010) A hybrid genetic based functional link artificial neural network with a statistical comparison of classifiers over multiple datasets. Neural Comput Appl 19(2):317–328

    Article  Google Scholar 

  11. Liu D (2014) Network site optimization of reverse logistics for E-commerce based on genetic algorithm. Neural Comput Appl 25:67–71

    Article  Google Scholar 

  12. Ilgin MA, Gupta SM (2010) Environmentally conscious manufacturing and product recovery (ECMPRO): a review of the state of the art. J Environ Manage 91(3):563–591

    Article  Google Scholar 

  13. Cordova J, Yu W (2013) Two types of haar wavelet neural networks for nonlinear system identification. Neural Process Lett 35(3):283–300

    Article  Google Scholar 

  14. Kerh T, Gunaratnam D, Chan YL (2010) Neural computing with genetic algorithm in evaluating potentially hazardous metropolitan areas result from earthquake. Neural Comput Appl 19(4):521–529

    Article  Google Scholar 

  15. Acampora G, Gaeta M, Loia V (2011) Hierarchical optimization of personalized experiences for e-Learning systems through evolutionary models. Neural Comput Appl 20(5):641–657

    Article  Google Scholar 

  16. Dash NB et al (2010) Hybrid neural modeling for groundwater level prediction. Neural Comput Appl 19(8):1251–1263

    Article  Google Scholar 

  17. Dehuri S, Cho SB (2010) A hybrid genetic based functional link artificial neural network with a statistical comparison of classifiers over multiple datasets. Neural Comput Appl 19(2):317–328

    Article  Google Scholar 

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Acknowledgments

The research on which this paper reports has been financially supported by Natural Science Foundation of Zhejiang Province by Project LY15G020021and Zhejiang Provincial Social Science Fund by Project 15JDXX02YB.

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Authors and Affiliations

  1. School of Management, Hangzhou Dianzi University, No. 1st, 2nd Ave. Xiasha Education Zone, Hangzhou, 310018, Zhejiang, People’s Republic of China

    Dawei Liu, Saifeng Cai & Xiaohong Guo

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  1. Dawei Liu
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  2. Saifeng Cai
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  3. Xiaohong Guo
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Correspondence to Dawei Liu.

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Liu, D., Cai, S. & Guo, X. Incremental sequential pattern mining algorithms of Web site access in grid structure database. Neural Comput & Applic 28, 575–583 (2017). https://doi.org/10.1007/s00521-015-2096-4

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  • DOI: https://doi.org/10.1007/s00521-015-2096-4

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