Abstract
In recent times, data mining plays one of the crucial aspects in business intelligence tasks by extracting useful pattern and future prediction. Cloud computing, on the other hand, is a topical trend in the field of providing computing resources as a service over the network. Combining data mining in cloud computing is a recent trend in knowledge discovery field as because no large number of resolutions are effusively accomplished and accessible to the cloud clients. This paper presents the basic concepts of data mining in cloud framework along with relevant significant works done in this field. Different frameworks along with approaches for diverse data mining tasks have been surveyed and presented in detail.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ambulkar, B., Borkar, V.: Data mining in cloud computing. Int. J. Comput. Appl. 23–26 (2012)
Geng, X., Yang, Z.: Data mining in cloud computing. In: Proceedings of International Conference on Information Science and Computer Applications ISCA, 2013, pp. 1–7
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 (2012)
Chu, C.T., Kim, S.K., Lin, Y.A., Yu, Y.Y., Bradski, G., Ng, A.Y., Olukotun, K.: Map-reduce for machine learning on multicore. Adv. Neural Inf. Process. Syst. J. 19, 281–287 (2007)
Patil, V., Nikam, V.B.: Study of Data mining algorithm in cloud computing using MapReduce Framework. J. Eng. Comput. Appl. Sci. 2(7), 65–70 (2013)
Vrbic, R.: Data mining and cloud computing. J. Inf. Technol. Appl. 75–87 (2012)
Grossman, R., Gu, Y.: Data mining using high performance data clouds: experimental studies using sector and sphere. In: Proceedings of 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, USA, 2008, pp. 920–927
Park, J.S., Chen, M., Yu, P.S.: An effective hash-based algorithm for mining association rules. In: Carey, M., Schneider, D. (eds.) Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 175–186. ACM, New York, USA (1995)
Li, N., Zeng, L., He, Q., Shi, Z.: Parallel implementation of Apriori algorithm based on MapReduce. Int. J. Netw. Distrib. Comput. 1(2), 89–96 (2013)
Li, J., Roy, P., Khan, S.U., Wang, L., Bai, Y.: Data mining using clouds an experimental implementation of Apriori over map-reduce. In: Proceedings of 12th IEEE International Conference ScalCom, Dec 2012
Cheung, D.W., et al.: A fast distributed algorithm for mining association rules. In: Proceedings of Parallel and Distributed Information Systems, pp. 31–42. IEEE CS Press (1996)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. Data Mining Knowl. Discov. J. USA 8(1), 53–87 (2004)
Zhou, L., Wang, X.: Research of the FP-growth algorithm based on cloud environment. J. Softw. 9(3) (2014)
Apiletti, D., Baralis, E., Cerquitelli, T., Chiusano, S., Grimaudo, L.: SEARUM: a cloud-based service for association rule mining. In: Proceedings of 12th IEEE International Conference ISPA, Melbourne, July 2013, pp. 1283–1290
Mahendiran, A., Saravanan, N., Sairam, N., Subramanian, V.: Implementation of K-Means clustering in cloud computing environment. Res. J. Appl. Sci. Eng. Technol. 4, 1391–1394 (2012)
Shindler, M., Wong, A., Meyerson, A.: Fast and accurate k-means for large datasets. In: Proceedings of Advances in Neural Information Processing Systems NIPS, pp. 2375–2383 (2011)
Dutta, S., Ghatak, S., Ghosh, S., Das, A.K.: A genetic algorithm based tweetclustering technique. In: 2017 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–6 (2017)
Srivastava, K., Shah, R., Swaminarayan, H., Valia, D.: Data mining using hierarchical agglomerative clustering algorithm in distributed cloud computing environment. Int. J. Comput. Theory Eng. 5(3) (2013)
Panchal, B., Kapoor, R.K.: Performance enhancement of cloud computing. Int. J. Eng. Adv. Technol. 2(5) (2013)
Panchal, B., Kapoor, R.: Dynamic VM allocation algorithm using clustering in cloud computing. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(9) (2013)
Catak, F.O., Balaban, M.E.: CloudSVM: training an SVM classifier in cloud computing systems. Comput. Res. Repos. J. (2013). arXiv:1301.0082
Zhou, L., Wang, H., Wang, W.: Parallel implementation of classification algorithms based on cloud computing environment. Indones. J. Electr. Eng. 10(5), 1087–1092 (2012)
Ding, J., Yang, S.: Classification rules mining model with genetic algorithm in cloud computing. Int. J. Comput. Appl. 48(18), 888–975 (2012)
Zhang, L., Zhao, S.: The strategy of classification mining based on cloud computing. In: Proceedings of International Workshop on Cloud Computing and Information Security, pp. 57–60 (2013)
Wang, J.: A novel K-NN classification algorithm for privacy preserving in cloud computing. Res. J. Appl. Sci. Eng. Technol. 4(22), 4865–4870 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sarkar, A., Bhattacharya, A., Dutta, S., Parikh, K.K. (2019). Recent Trends of Data Mining in Cloud Computing. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 813. Springer, Singapore. https://doi.org/10.1007/978-981-13-1498-8_50
Download citation
DOI: https://doi.org/10.1007/978-981-13-1498-8_50
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1497-1
Online ISBN: 978-981-13-1498-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)