Author : C Arun Kumar 1
Date of Publication :21st June 2018
Abstract: PCM is one of the methods used for C means clustering process in which there are two types of clustering, normal PCM clustering and important is high order PCM for big data clustering. The HOPCM method based on Map reduces for the large amount of the heterogeneous data is used. Finally a privacy preserving high-order possibilistic c-means algorithm to protect the private data on cloud by applying the background verification scheme to HOPCM a high-order PCM algorithm to tackle big data clustering by making the objective function minimal in the tensor space. Clustering is designed to separate objects into several different groups according to special metrics, making the objects with similar features in the same group. Clustering techniques have been successfully applied to knowledge discovery and data engineering. With the increasing popularity of big data, big data clustering is attracting much attention from data engineers and researchers
- X. Wu, X. Zhu, G.-Q. Wu, and W. Ding, ”Data Mining with Big Data,” IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 1, pp. 97-107, Jan. 2014.
- B. Ermis, E. Acar, and A. T. Cemgil, ”Link Prediction in Heterogeneous Data via Generalized Coupled Tensor Factorization,” Data Mining and Knowledge Discovery, vol. 29, no. 1, pp. 203-236, 2015.
- Q. Zhang, L. T. Yang, and Z. Chen, ”Deep Computation Model for Unsupervised Feature Learning on Big Data,” IEEE Transactions on Services Computing, vol. 9, no. 1, pp. 161-171, Jan. 2016.
- N. Soni and A. Ganatra, ”MOiD (Multiple Objects Incremental DBSCAN) - A Paradigm Shift in Incremental DBSCAN,” International Journal of Computer Science and Information Security, vol. 14, no. 4, pp. 316-346, 2016.
- Z. Xie, S. Wang, and F. L. Chung, ”An Enhanced Possibilistic c-Means Clustering Algorithm EPCM,” Soft Computing, vol. 12, no. 6, pp. 593-611, 2008.
- Q. Zhang, C. Zhu, L. T. Yang, Z. Chen, L. Zhao, and P. Li, ”An Incremental CFS Algorithm for Clustering Large Data in Industrial Internet of Things,” IEEE Transactions on Industrial Informatics, 2015. DOI: 10.1109/TII.2017.2684807.
- X. Zhang, ”Convex Discriminative Multitask Clustering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 1, pp. 28-40, Jan. 2015.
- B. Gao, T. Liu, T. Qin, X. Zheng, Q. Cheng, and W. Ma, ”Web Image Clustering by Consistent Utilization of Visual Features and Surrounding Texts,” in Proceedings of the 13th Annual ACM International Conference on Multimedia, 2005, 112-121.