Refine your search
Collections
Co-Authors
Journals
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Mittal, Ruchi
- Collaborative Approach for Trend Analysis Using Clustering Mechanisms and Big Data Technologies
Abstract Views :211 |
PDF Views:2
Authors
Affiliations
1 Division of Computer Engineering, Netaji Subhas Institute of Technology, IN
1 Division of Computer Engineering, Netaji Subhas Institute of Technology, IN
Source
ICTACT Journal on Soft Computing, Vol 8, No 4 (2018), Pagination: 1701-1705Abstract
The rapid growth in technologies and social media provides us the enormous amount of data, and it opens a wider window for researchers to work on such data. One of the critical analyses of the data is to check the changing trends in data. These days, massive volumes of data are being generated and processed using Hadoop and its ecosystem tools. These tools help in fast and efficient computing of a significant amount of data. In this paper, we collaborate few popular clustering algorithms with big data technologies to analyze the usage of mobile phones and networks in various locations. We loaded and processed this dataset in Apache Hive to examine the number of users and most prominent systems in given areas, based on their location codes. Further, we compared the time taken to build the clustered model on our framework to that on Weka tool. It was observed that Weka takes comparatively longer to process the dataset. This analysis would not only help in management and segregation of a considerable amount of data but would also help mobile service providers to understand the patterns of usage by customers and network problems, which may persist in some regions.Keywords
Big Data, Clustering Methods, Machine Learning, Hive.
References
- M. Chen, A. Ludwig and K. Li, “Clustering in Big Data”, Available at: https://pdfs.semanticscholar.org/2ab0/d4ded091959f0ed7140b85c90bef49d9ab1b.pdf.
- M. Hajeer and D. Dasgupta, “Handling Big Data using a Data-Aware HDFS and Evolutionary Clustering Technique”, IEEE Transactions on Big Data, 2017.
- A. Elsayed, O. Ismail and M. El-Sharkawi, “MapReduce: State-of-the-Art and Research Directions”, International Journal of Computer and Electrical Engineering, Vol. 6, No. 1, pp. 34-39, 2014.
- R. Loohach and K. Garg, “Effect of Distance on K-means Clustering Algorithm”, International Journal of Computer Applications, Vol. 5, No. 2, pp. 7-9, 2012.
- I. Foster, C. Kesselman, J. Nick and S. Tuecke, “The Physiology of the Grid: an Open Grid Services Architecture for Distributed Systems Integration”, Technical Report, Department of Information Science, University of Southern California, 2002.
- Aayushi Bindal and Analp Pathak, “Survey on K-means Clustering and Web-Text Mining”, International Journal of Science and Research, Vol. 5, No. 4, pp. 1049-1052, 2016.
- Min Huang, Lei Yu and Ying Chen, “Improved K-Means Clustering Center Selecting Algorithm”, Information Engineering and Applications, pp. 373-379, 2012.
- S. Seo and K. Obermayer, “Self-Organizing Maps and Clustering Methods for Matrix Data”, Neural Networks, Vol. 17, No. 8-9, pp. 1211-1229, 2004.
- Y. Rani and D. Rohil, “A Study of Hierarchical Clustering Algorithm”, International Journal of Information and Computation Technology, Vol. 3, No. 10, pp. 1225-1232, 2013.
- C. Selvi and E. Sivasankar, “A Novel Optimization Algorithm for Recommender System using Modified Fuzzy C-means Clustering Approach”, Soft Computing, pp. 1-16, 2017.
- S. Mehta and V. Mehta, “Hadoop Ecosystem: An Introduction”, International Journal of Science and Research, Vol. 5, No. 6, pp. 557-562, 2017.
- R. Yadav and A. Sharma, “Advanced Methods to Improve Performance of K-Means Algorithm: A Review”, Global Journal of Computer Science and Technology, Vol. 12, No. 9, pp. 47-52, 2012.
- R.V. Singh and M.P.S Bhatia, “Data Clustering with Modified K-means Algorithm”, Proceedings of International Conference on Recent Trends in Information Technology, pp. 55-59, 2011.
- Automated Cryptocurrencies Prices Prediction Using Machine Learning
Abstract Views :206 |
PDF Views:2
Authors
Affiliations
1 Division of Computer Engineering, Netaji Subhas Institute of Technology, IN
2 Division of Computer Engineering, Netaji Subhas Institute of Technology
1 Division of Computer Engineering, Netaji Subhas Institute of Technology, IN
2 Division of Computer Engineering, Netaji Subhas Institute of Technology
Source
ICTACT Journal on Soft Computing, Vol 8, No 4 (2018), Pagination: 1758-1761Abstract
Currently, Cryptocurrency is one of the trending areas of research among researchers. Many researchers may analyze the cryptocurrency features in several ways such as market price prediction, the impact of cryptocurrency in real life and so on. In this paper, we focus on market price prediction of the number of cryptocurrencies based on their historical trend. For our study, we tried to understand and identify the daily trends in the cryptocurrency market which analyzing the features related to the price of cryptocurrency. Our dataset consists of over nine features relating to the cryptocurrency price recorded daily over the period of 6 months. We applied some machine-learning algorithms to predict the daily price change of cryptocurrencies.Keywords
Cryptocurrency, Bitcoin, Decentralization, Network, Price Prediction.References
- S. Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System”, Available at: https://bitcoin.org/bitcoin.pdf, Accessed on 2008.
- D. Garcia, C.J. Tessone, P. Mavrodiev and N. Perony, “The Digital Traces of Bubbles: Feedback Cycles between Socio-Economic Signals in the Bitcoin Economy”, Journal of the Royal Society Interface, Vol. 11, No. 99, pp. 1-28, 2014.
- D. Ron and A. Shamir, “Quantitative Analysis of the Full Bitcoin Transaction Graph”, Proceedings of International Conference on Financial Cryptography and Data Security, pp. 6-24, 2013.
- F. Reid and M. Harrigan, “An Analysis of Anonymity in the Bitcoin System”, Proceedings of IEEE International Conference on Privacy, Security, Risk, and Trust, pp. 1318-1326, 2013.
- R. Grinberg, “Bitcoin: An Innovative Alternative Digital Currency”, Hastings Science and Technology Law Journal, Vol. 4, pp. 160-165, 2012.
- M. Levine, “Bitcoin Is an Expensive Way to Pay for Stuff”, Available at: https://www.bloomberg.com/view/articles/2014-01-02/bitcoin-is-an-expensive-way-to-pay-for-stuff.
- D. Yermack, “Is Bitcoin a Real Currency?”, The Handbook of Digital Currency, 2013.
- A. Badev and M. Chen, “Bitcoin: Technical Background and Data Analysis”, Available at: https://www.federalreserve.gov/econresdata/feds/2014/files/2014104pap.pdf.
- M. Iwamura, Y. Kitamura and T. Matsumoto, “Is Bitcoin the Only Cryptocurrency in the Town? Economics of Cryptocurrency and Friedrich A. Hayek”, Discussion Paper Series, Institute of Economic Research, Hitotsubashi University, pp. 1-13, 2014.
- C. Gunduz and B. Donald, “A Theoretical Foundation for Technical Analysis”, Journal of Technical Analysis, Vol. 59, No. 5, pp. 1-22, 2003.
- L. Andrew, H. Mamaysky and J. Wang, “Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation”, Journal of Finance, Vol. 55, No. 4, pp. 1705-1765, 2000.