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Research and Citation Analysis of Data Mining Technology Based on Bayes Algorithm

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Abstract

With the development of social information technology and the increasing of information data in big data era, how to query the required data accurately is becoming more and more important, the purpose of this paper is to establish a model of data mining technology. In this paper, we use the Bayesian network learning model to study the data mining technology. In this paper, a Bayesian network learning model is established, then, the parameters of the recognition and the selection of coefficients are analyzed in detail, after that, the data mining model based on Bayesian computation is deduced, and the reliability of the model is verified by the example of the students. The probability distribution pattern used by Bayes has many advantages in data mining. It further proves the applicability of Bayesian formula, and provides a reference for data mining technology.

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References

  1. Thangaraju Mr P., and Mehala R. (2015) Novel Classification based approaches over Cancer Diseases. system 4.3

  2. Bijalwan V, Kumar V, Kumari P et al (2014) KNN based machine learning approach for text and document mining. Int J Database Theory Appl 7(1):61–70

    Article  Google Scholar 

  3. Yukselturk E, Ozekes S, Türel YK (2014) Predicting dropout student: an application of data mining methods in an online education program. Eur J Open, Distance e-Learning 17(1):118–133

    Google Scholar 

  4. Bala S, Kumar K. (2014) A literature review on kidney disease prediction using data mining classification technique[J]

  5. He W, Yan G, Da Xu L (2014) Developing vehicular data cloud services in the IoT environment. IEEE Trans Ind Inf 10(2):1587–1595

    Article  Google Scholar 

  6. Peña-Ayala A (2014) Educational data mining: a survey and a data mining-based analysis of recent works. Expert Syst Appl 41(4):1432–1462

    Article  Google Scholar 

  7. Abdelhamid N, Ayesh A, Thabtah F (2014) Phishing detection based associative classification data mining. Expert Syst Appl 41(13):5948–5959

    Article  Google Scholar 

  8. Chen F, Deng P, Wan J et al (2015) Data mining for the internet of things: literature review and challenges. Int J Distrib Sens Netw 501:431047

    Article  Google Scholar 

  9. Dhakar M, Tiwari A (2014) A novel data mining based hybrid intrusion detection framework. J Inf Comput Sci 9(1):037–048

    Google Scholar 

  10. Zhang ML, Zhou ZH (2014) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819–1837

    Article  Google Scholar 

  11. Chaurasia V, Pal S (2014) Data mining approach to detect heart diseases. Int J Adv Comput Sci Info Technol (IJACSIT) 2:56–66

    Google Scholar 

  12. Jelinek HF, Yatsko A, Stranieri A et al (2014) Novel data mining techniques for incomplete clinical data in diabetes management. British JAppl Sci Technol 4(33):4591–4460

    Article  Google Scholar 

  13. Bijalwan V, Kumari P, Pascual J, et al. (2014) Machine learning approach for text and document mining[J]. arXiv preprint arXiv:1406.1580

  14. Xing W, Guo R, Petakovic E et al (2015) Participation-based student final performance prediction model through interpretable genetic programming: integrating learning analytics, educational data mining and theory. Comput Hum Behav 47:168–181

    Article  Google Scholar 

  15. Okazaki S, Díaz-Martín AM, Rozano M et al (2015) Using twitter to engage with customers: a data mining approach. Internet Res 25(3):1066–2243

    Article  Google Scholar 

  16. Bounhas M, Hamed MG, Prade H et al (2014) Naive possibilistic classifiers for imprecise or uncertain numerical data. Fuzzy Sets Syst 239:137–156

    Article  MathSciNet  MATH  Google Scholar 

  17. Charninda T, Dayaratne TT, Amarasinghe HKN, et al. (2014) Content based hybrid sms spam filtering system[J]

  18. Shukla DP, Patel SB, Sen AK (2014) A literature review in health informatics using data mining techniques. Int J Softw Hardw Res Eng 2(2):123–129

    Google Scholar 

  19. Olsson A, Nordlöf D. (2015) Early screening diagnostic aid for heart disease using data mining: An evaluation using patient data that can be obtained without medical equipment[J]

  20. Zhou X, Lim JS, Kwon IK, et al. (2014) EM algorithm with GMM and Naive Bayesian to Implement Missing Values[J]. Proceedings of April 17th, 15–19

  21. Dey M, Rautaray SS. (2014) Disease Predication of Cardio-Vascular Diseases, Diabetes and Malignancy in Lungs Based on Data Mining Classification Techniques[J]

  22. Jayakameswaraiah M, Ramakrishna S. (2014) A study on prediction performance of some data mining algorithms[J]. International Journal, 2 (10)

  23. Xiao-feng Z, Shu W (2014) Data mining method of road transportation management information based on rough set and association rule. J South China Univ Technol (Natural Science Edition) 2:021

    Google Scholar 

  24. Zhao Y, Niu Z, Peng X (2014) Research on data Mining Technologies for Complicated Attributes Relationship in digital library collections. Appl Math 8(3):1173–1178

    Google Scholar 

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Acknowledgements

This work is supported by new technology development projects of Jilin Provincial Science & Technology Department, No: 20130305020GX.

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Correspondence to Ming Qu.

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Liu, M., Qu, M. & Zhao, B. Research and Citation Analysis of Data Mining Technology Based on Bayes Algorithm. Mobile Netw Appl 22, 418–426 (2017). https://doi.org/10.1007/s11036-016-0797-2

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  • DOI: https://doi.org/10.1007/s11036-016-0797-2

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