Data Mining Techniques based on a Cloud Computing

Walid Qassim Qwaider

doi:10.29040/ijcis.v5i3.187

Data Mining Techniques based on a Cloud Computing

Walid Qassim Qwaider

Abstract

Data mining is an essential process as it is used to find and discover new, correct, useful and understandable forms of data. Cloud computing has become a multi-use technology for data processing, storage, and distribution, offering a wide range of applications and infrastructure such as the Internet service that customers can access from anywhere. The massive volume of data can have stored in low-cost cloud data centers. Both data mining and cloud computing technologies help business organizations maximize profits and reduce costs in different ways. The focus of this paper on data mining is the technique of knowing the previously unknown relationship and new patterns in extensive data that can even predict future decisions by using some useful algorithms and techniques such as classification, prediction, clustering, summarization, base assemblies, regression analysis, time series analysis.

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References

Anwaar Ali, Raihan ur Rasool, Arjuna Sathiaseelan, Andrej Zwitter. (2016). Big Data For Development: Applications and Techniques. National University of Sciences and Technology (NUST), Pakistan,arXiv:1602.07810v1.

Ahmed S, Maria S. (2014). Cloud Computing: Paradigms and Technologies. F. Xhafa and N. Bessis (eds.), Inter-cooperative Collective Intelligence: Techniques and Applications, Studies in Computational Intelligence 495 ,DOI: 10.1007/978-3-642-35016-0_2, Springer-Verlag Berlin Heidelberg.

Khadir Mohideen. (2015). Survey of data mining techniques (DMT) in Cloud Computing. International Journal of Scientific Engineering and Applied Science (IJSEAS) - Volume-1, Issue-8, ISSN: 2395-3470.

Mahendiran. A. (2012). Implementated of K-Means Clustering in Cloud Computing Environment. Research Journal of Applied Sciences, Engineering, and Technology 4(10): 1391-1394, 2012 ISSN: 2040-7467.

Sonal M., Kanwal G. (2013). Factors Affecting Efficiency of K-means Algorithm. International Journal of Advancements in Research & Technology, Volume 2, Issue5, 85 ISSN 2278-7763.

Raghavendra K. ,Pramod K., Arun A, Raghavendra C., Rajkumar B. (2015). The anatomy of big data computing. Raghavendra Kune, Department of Space, Advanced Data Processing Research Institute, Hyderabad, India.

Patidar A., Patidar V. (2015). Analysis of Solving E-Learning Problems (ASELP) using Cloud Computing. International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, Issue 11.

J. R. Jeba, D. S. Misbha. (2016). A Study of Data Warehousing on Cloud Environment, International Journal of Innovative Research in Science, Engineering and Technology An ISO 3297: 2007 Certified Organization Vol. 5, Issue 7.

Aishwarya S. Pati1.Ankita S. Patil. (2015). A review of Data Mining based Cloud Computing. International Journal of Research In Science & Engineering e-ISSN: 2394-8299, Volume: 1 Special Issue: 1, p-ISSN: 2394-8280.

R.Kabilan, N.Jayaveeran.(2015). A Review of Data Mining in Cloud Computing Environment, International Journal of Innovative Research in Computer and Communication Engineering. Vol. 3, Issue 10. ISSN(Online): 2320-9801.

Petar Ristoski, Heiko Paulheim. (2016). Semantic Web (SW) in data mining and knowledge discovery (KD): A comprehensive survey Web Semantics, 36, 1–22.

Thirunavukkarasu K., Digvijay S., Mohd. A., Ajay S., Singh. (2016). Data Mining Techniques in Cloud Computing: A Survey. International Journal of Recent Trends in Engineering & Research Volume 02, Issue 03; ISSN: 2455-1457.

Juan H., Mercedes P. (2017). Cloud Comuting implementation of the K-means algorithm for hyperspectral image analysis. J Supercomputer 73:514–529 DOI 10.1007/s11227-016-1896-3.

Xia Geng, Zhi Yang. (2013). Data Mining in Cloud Computing. School of Computer Science (SCS) and Telecommunication Engineering , Jiangsu University, Jiangsu Zhenjiang, P.R. China, Atlantis Press.

D. Talia and P. Trunfio, How distributed data mining tasks can thrive as knowledge services Communications of the ACM. 53(2010) 132-137.

Mell, Peter; Grance, Timotht.(2011). The NIST definition of Cloud Computing (CC): Recommendations of the National Institute of Standards and Technology.NIST National Institute of Standards and Technology.

Aris-Kyriakos Koliopoulos, Paraskevas Yiapanis, Firat Tekiner, Goran Nenadic, John Keane. (2015). A Parallel Distributed Weka Framework for Big Data Mining using Spark. IEEE International Congress on Big Data, 978-1-4673-7278-7/15, DOI 10.1109/Bigdata Congress.

S. Das, Y. Sismanis, K. S. Beyer, R. Gemulla, P. J. Haas, and J. McPherson. (2010). Ricardo: Integrating R and Hadoop. in Intl Conf. on Management of Data, 2010, pp. 987–998.

Haritha Padmanabhan, Derroll David. (2017). A Survey on Efficiency in Big Data Mining. International Journal of Advanced Research in Computer (IJARC) and Communication Engineering, ISSN 2278-1021.

E. R. Sparks, A. Talwalkar, V. Smith, J. Kottalam, X. Pan, J. E. Gonzalez, M. J. Franklin, M. I. Jordan, and T. Kraska, “MLI: an API for distributed machine learning,” ICDM, 2013.

Unnati R. Raval, Chaita Jani. (2016). Implementing & Improvisation of K-means Clustering Algorithm. International Journal of Computer Science (IJCS) and Mobile Computing, Vol.5 Issue.5, pg. 191-203.

Anca A., Florina P., Geanina U., George S., Gyorgy T. (2014). Study on advantages and disadvantages of CC – the benefits of Telemetry Applications in the Cloud. Recent Advances in Applied Computer Science (RAACS) and Digital Services, ISBN: 978-1-61804-179-1

DOI: https://doi.org/10.29040/ijcis.v5i3.187