Abstract
Education in artificial intelligence attracts increasing attention. Data mining is an important subject in artificial intelligence. Cloud Computing can help on providing resources for education, which motivates a data mining as a cloud service (DMCS) for facilitating the learning of data mining. However there exists few DMCS, where user-friendly and easy-to-use are critical for students to access the services. Therefore in this paper, we propose the concept of data mining as a cloud service as an answer to tackle this issue. The proposed DMCS consists of all necessary steps for data mining, including data fusion and pre-processing, a comprehensive machine learning library including common algorithms and deep learning algorithms, graphical presentation of the mining results. The whole mining process has a user-friendly graphical user interface for beginners to facilitate the learning process. The demo preliminarily analyzes the power used by the DMCS service and shows the DMCS service has an outstanding effect.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Barga, R., Fontama, V., Tok, W.H.: Predictive Analytics with Microsoft Azure Machine Learning, pp. 21–42 (2014)
Villegas-Ch, W., Luján-Mora, S.: Analysis of data mining techniques applied to LMS for personalized education. In: IEEE World Engineering Education Conference (EDUNINE), pp. 85–89. IEEE (2017)
Blikstein, P., Worsley, M.: Multimodal learning analytics and education data mining: using computational technologies to measure complex learning tasks. J. Learn. Anal. 3(2), 220–238 (2016)
Angeli, C., Howard, S., Ma, J., Yang, J., Kirschner, P.A.: Data mining in educational technology classroom research: can it make a contribution? Comput. Educ. 113, 226–242 (2017)
Bhise, R., Thorat, S., Supekar, A.: Importance of data mining in higher education system. IOSR J. Hum. Soc. Sci. (IOSR-JHSS) 6(6), 18–21 (2013). ISSN: 2279-0837
Zhang, W., Xu, L., Li, Z., Lu, Q., Liu, Y.: A deep-intelligence framework for online video processing. IEEE Softw. 33(2), 44–51 (2016)
Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)
Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. HotCloud 10(10), 95 (2010)
Vavilapalli, V.K., Murthy, A.C., Douglas, C., Agarwal, S., Konar, M., Evans, R., Graves, T., Lowe, J., Shah, H., Seth, S., et al.: Apache Hadoop Yarn: yet another resource negotiator. In: Proceedings of the 4th Annual Symposium on Cloud Computing, p. 5. ACM (2013)
Chen, T., Guestrin, C.: XGboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. ACM (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Zhang, W., Lv, H., Xu, L., Liu, X., Zhou, J. (2018). Data Mining as a Cloud Service for Learning Artificial Intelligence. In: Satoh, S. (eds) Image and Video Technology. PSIVT 2017. Lecture Notes in Computer Science(), vol 10799. Springer, Cham. https://doi.org/10.1007/978-3-319-92753-4_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-92753-4_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92752-7
Online ISBN: 978-3-319-92753-4
eBook Packages: Computer ScienceComputer Science (R0)