Skip to main content
SpringerLink
Log in

RETRACTED ARTICLE: Text mining and sustainable clusters from unstructured data in cloud computing

  • Published:
Cluster Computing Aims and scope Submit manuscript

This article was retracted on 21 December 2022

This article has been updated

Abstract

Text mining (TM) is basically the Data mining on information. TM is a procedure of separating possibly helpful data from crude Data, to enhance the nature of the data benefit. The manuscript presents the essential idea of CC and TM firstly, and outlines out how TM is utilized as a part of CC. There is an enormous measure of consideration being cantered around enhancing the security applications in the web nowadays. The Internet measurements demonstrate that there were numerous sources that significantly rely on upon access to appropriate and secure. Determination part of the issue has been examined for long, so Author sets out taking a shot at the to begin with, while the remaining is still in thought organize. Author gives a bit of knowledge into the proposed work on robotizing therapeutic determination utilizing mining strategies and incorporates some underlying outcomes. A principled methodology is proposed to build up a keen data framework by breaking down the formless information. Develop the blame philosophy for discover the blame so that concentrate the superfluous data. The proposed technique is that investigation of rundown and literary theft examination and discovers the productivity that is the time multifaceted nature and increment the execution of framework utilizing group based methodology.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Change history

References

  1. Bhaduri, K., Das, K., Liu, K., Kargupta, H., Ryan, J.: Distributed data mining bibliography. Distrib. Data Min. Bibliogr. (2011)

  2. Bradley, B.D., Qu, S., Cheng, Y.L., Peel, D., Howie, S.R.: Options for medical oxygen technology systems in low-resource settings: a framework for comparison. In: IEEE Global Humanitarian Technology Conference (GHTC), pp. 356–362 (2012)

  3. Chen, B., Lam, W., Tsang, I.W., Wong, T.-L.: Discovering low-rank shared concept space for adapting text mining models. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1284–1297 (2013)

    Article  Google Scholar 

  4. Chen, Y., Li, F., Fan, J.: Mining association rules in big data with NGEP. Clust. Comput. 18(2), 577–585 (2015)

    Article  Google Scholar 

  5. Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

  6. Geng, X., Yang, Z.: Data mining in cloud computing. In: Proceedings of the 2013 International Conference on Information Science and Computer Applications (ISCA) (2013)

  7. Gobbel, G.T., Reeves, R., Jayaramaraja, S., Giuse, D., Speroff, T., Brown, S.H., Elkin, P.L., Matheny, M.E.: Development and evaluation of RapTAT: a machine learning system for concept mapping of phrases from medical narratives. J. Biomed. Inform. 48, 54–65 (2014)

    Article  Google Scholar 

  8. Hartsock, B., MacLeod, B., Roberge, D., Asangansi, I.: Software extensibility strategies for health and demographic systems in low-income countries. In: IEEE Global Humanitarian Technology Conference (GHTC), pp. 512–517 (2011)

  9. Hischier, R., Wäger, P.A.: The transition from desktop computers to tablets: a model for increasing resource efficiency?. In: ICT Innovations for Sustainability, pp. 243–256. Springer, Berlin (2015)

  10. Jiang, J.-Y., Liou, R.-J., Lee, S.-J.: A fuzzy self-constructing feature clustering algorithm for text classification. IEEE Trans. Knowl. Data Eng. 23(3), 335–349 (2011)

    Article  Google Scholar 

  11. Jin, Z., Wang, X., Gui, Q., Liu, B., Song, S.: Improving diagnostic accuracy using multiparameter patient monitoring based on data fusion in the cloud. In: Future Information Technology, pp. 473–476. Springer, Berlin (2014)

  12. Logothetis, D., Olston, C., Reed, B., Webb, K.C., Yocum, K.: Stateful bulk processing for incremental analytics. In: Proceedings of the 1st ACM Symposium on Cloud Computing, pp. 51–62. ACM, New York (2010)

  13. Mohammed, O., Benlamri, R., Fong, S.: Building a diseases symptoms ontology for medical diagnosis: an integrative approach. In: International Conference on Future Generation Communication Technology (FGCT), pp. 104–108 (2012)

  14. Ngufor, C., Wojtusiak, J., Hooker, A., Oz, T., Hadley, J.: Extreme logistic regression: a large scale learning algorithm with application to prostate cancer mortality prediction. In: FLAIRS Conference (2014)

  15. Pendyala, V.S., Holliday, J.: Cloud as a computer. In: Advanced Design Approaches to Emerging Software Systems: Principles, Methodologies and Tools, pp. 241–249. IGI Global, Hershey (2012)

  16. Tang, Z., Jiang, L., Yang, L., Li, K., Li, K.: CRFs based parallel biomedical named entity recognition algorithm employing MapReduce framework. Clust. Comput. 18(2), 493–505 (2015)

  17. Rahman, M.Z.U., Shaik, R.A., Reddy, D.R.K.: A non-linearities based noise canceler for cardiac signal enhancement in wireless health care monitoring. In: IEEE Global Humanitarian Technology Conference (GHTC), pp. 288–292 (2012)

  18. Sarwar, A., Sharma, V.: Comparative analysis of machine learning techniques in prognosis of type II diabetes. AI Society 29(1), 123–129 (2014)

    Article  Google Scholar 

  19. Scheuermann, R.H., Ceusters, W., Smith, B.: Toward an ontological treatment of disease and diagnosis. In: Proceedings of the 2009 AMIA Summit on Translational Bioinformatics, pp. 116–120 (2009)

  20. Shehata, S., Karray, F., Kamel, M.: An efficient concept-based mining model for enhancing text clustering. IEEE Trans. Knowl. Data Eng. 22(10), 1360–1371 (2010)

    Article  Google Scholar 

  21. Singh, P., Kulkarni, S., Keech, E., McDermott-Levy, R., Klingler, J.: Making health care more accessible to rural communities in Waslala, Nicaragua using low-cost telecommunications. In: IEEE Global Humanitarian Technology Conference (GHTC), pp. 194–200 (2011)

  22. Smolinska, A., Hauschild, A.C., Fijten, R.R.R., Dallinga, J.W., Baumbach, J., Van Schooten, F.J. Current breathomics–a review on data pre-processing techniques and machine learning in metabolomics breath analysis. J. Breath Res. 8(2), 027105 (2014)

  23. Talia, D., Trunfio, P.: How distributed data mining tasks can thrive as knowledge services. Commun. ACM 53(7), 132–137 (2010)

    Article  Google Scholar 

  24. Talia, D., Trunfio, P., Verta, O.: The Weka4WS framework for distributed data mining in service-oriented Grids. Concurr. Comput. 20(16), 1933–1951 (2008)

    Article  Google Scholar 

  25. Veeraraghavan, S., Krishnaswamy, P.: A novel technology based framework to address global humanitarian issues. In: IEEE Global Humanitarian Technology Conference (GHTC), pp. 338–343 (2011)

  26. Yu, L., Zheng, J., Shen, W.C., Wu, B., Wang, B., Qian, L., Zhang, B.R.: BC-PDM: data mining, social network analysis and text mining system based on cloud computing. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1496–1499. ACM (2012)

  27. Zalzala, A.M., Raja, B.A., Prashar, S., Chia, S.: A feasibility study for the development of value added services for rural healthcare. In: IEEE Global Humanitarian Technology Conference (GHTC), pp. 294–299 (2013)

  28. Zhong, N., Li, Y., Sheng-Tang, W.: Effective pattern discovery for text mining. IEEE Trans. Knowl Data Eng. 24(1), 30–44 (2012)

    Article  Google Scholar 

Download references

Acknowledgements

This paper is supported by the Education and Research Project of Fujian Province (No. JA15877); Science and Technology Project of Xiamen (Nos. 3502Z20163015 and 3502Z20163014).

Author information

Authors and Affiliations

  1. Department of Automation, Xiamen University, Xiamen, China

    Ning Wang & Jianping Zeng

  2. Department of Information and Mechatronics Engineering, Xiamen Huaxia University, Xiamen, China

    Ning Wang & Mingming Chen

  3. Fujian Province Engineering Research Center on New Generation of Information and Communication Technology and Wisdom Education, Xiamen, China

    Ning Wang & Mingming Chen

  4. School of Computing and Information Sciences at Florida International University, Miami, FL, USA

    Ning Wang

  5. Department of Computer Science, Ningde Normal University, Ningde, China

    Maozhi Ye

  6. University of Illinois at Springfield, Springfield, USA

    Mingming Chen

Authors
  1. Ning Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianping Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maozhi Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mingming Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mingming Chen.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, N., Zeng, J., Ye, M. et al. RETRACTED ARTICLE: Text mining and sustainable clusters from unstructured data in cloud computing. Cluster Comput 21, 779–788 (2018). https://doi.org/10.1007/s10586-017-0909-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-0909-1

Keywords

Search

Navigation