Design and implementation data classification using fuzzy c-means based on hybrid clustering technique

Authors

  • Patidar N Information Technology, Patel College of Science and Technology, Indore
  • Patidar K Information Technology, Patel College of Science and Technology, Indore

Keywords:

Data clustering Algorithm, Portioning, Data Mining, Fuzzy C Mean, Hybrid FCM clustering algorithm

Abstract

The management and analysis of big data has been recognized as one of the majority significant promising requirements in recent years. This is because of the pure volume and growing complexity of data creature created or composed. Existing clustering algorithms cannot grip big data, and consequently, scalable solutions are essential. The experimental analysis will be accepted out to assess the practicability of the scalable Possibilistic Fuzzy CMeans (PFCM) clustering technique and partial Fuzzy C-Means (PFCM) clustering technique.. Two-stage and twophase fuzzy C-means (FCM) algorithms have been report. In this paper, to exhibit that the Hybrid FCM clustering algorithm (HFCM) can be enhanced by the utilization of static and dynamic single-pass incremental FCM measures. Proposed technique finds upper head over existing technique in terms of accuracy, classification error entitlement and time. To observed that multistage clustering can speed up convergence and advance clustering superiority.

References

. M. Omair Shafiq ,” Event Segmentation using MapReduce based Big Data Clustering” 2016 IEEE International Conference on Big Data (Big Data) 978-1-4673-9005-7/16/ ©2016 IEEE.

. Shwet Ketu, Sonali Agarwal,” Performance Enhancement of Distributed K-Means Clustering for Big Data Analytics Through Inmemory Computation” 978-1-4673-7948- 9/152015 IEEE.

. Fahad, A., Alshatri, N., Tari, Z., Alamri, A., Khalil, I., Zomaya, A. Y. and Bouras, A. (2014). A survey of clustering algorithms for big data: Taxonomy and empirical analysis, Emerging Topics in Computing, IEEE Transactions on, 2(3), 267-279 (2014)

. Shirkhorshidi, A. S., Aghabozorgi, S., Wah, T. Y., and Herawan, T, Big data clustering: A review. In Computational Science and Its ApplicationsICCSA, 707-720 (2014)

. Juby Mathew, R Vijayakumar, Ph. D,” Scalable Parallel Clustering Approach for Large Data using Possibilistic Fuzzy CMeans Algorithm” International Journal of Computer Applications (0975 – 8887) Volume 103 – No.9, October 2014.

. The Economist," Data, data everywhere," 25 February 2010. [Online]. Available: http://www.economist.com/node/15557443.

. P. Bhargavi, B. Jyothi, S. Jyothi, K. Sekar, “Knowledge Extraction Using Rule Based Decision Tree Approach”, IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.7, July 2008

. Xia Hu, Lei Tang, Jiliang Tang, Huan Liu, “Exploiting Social Relations for Sentiment Analysisin Microblogging”, WSDM ’13, February 4–8, 2013, Rome, Italy, Copyright 2013 ACM 978-1-4503-1869-3/13/02

. Zitao Liu, Wenchao Yu, Wei Chen, Shuran Wang, Fengyi Wu, “Short Text Feature Selection for Micro-blog Mining”, Conference Paper, January 2011, DOI: 10.1109/CISE.2010.5677015 • Source: IEEE Xplore

. Dhanesh Kothari, S. Thavasi Narayanan, K. Kiruthika Devi, “Extended Fuzzy c-means with Random Sampling Techniques for Clustering Large Data”, in International Journal of Innovative Research in Advanced Engineering, vol. 1, Issue. 1, March 2014.

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Published

2024-02-26

How to Cite

patidar, N., & Patidar, K. (2024). Design and implementation data classification using fuzzy c-means based on hybrid clustering technique. COMPUSOFT: An International Journal of Advanced Computer Technology, 7(06), 2793–2796. Retrieved from https://ijact.in/index.php/j/article/view/439

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Section

Original Research Article

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