Weitere Kapitel dieses Buchs durch Wischen aufrufen
Data stream mining is the process of extracting knowledge from continuously generated data. Since data stream processing is not a trivial task, the streams have to be analyzed with proper stream mining techniques. In many large volume of data stream processing, stream clustering helps to find the valuable hidden information. Many works have concentrated on clustering the data streams using various methods, but mostly those approaches lack in some core tasks needed to improve the cluster accuracy and quick processing of data streams. To tackle the problem of improving cluster quality and reducing the time for data stream processing time in cluster generation, the partition-based DBStream clustering method is proposed. The result has been compared with various data stream clustering methods, and it is evident from the experiments that the purity of clusters improves 5% and the time taken is reduced by 10% than the average time taken by other methods for clustering the data streams.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
X. Gao, E. Ferrara and J. Qiu, “Parallel Clustering of High-Dimensional Social Media Data Streams,” 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), Shenzhen, 2015, pp. 323–332.
A. Kaneriya and M. Shukla, “A novel approach for clustering data streams using granularity technique,” International Conference on Advances in Computer Engineering and Applications (ICACEA), Ghaziabad, 2015, pp. 586–590.
G. Lin and L. Chen, “A Grid and Fractal Dimension-Based Data Stream Clustering Algorithm,” International Symposium on Information Science and Engineering, Shanghai, 2008, pp. 66–70.
A. Amini, H. Saboohi and T. Y. Wah, “A Multi Density-Based Clustering Algorithm for Data Stream with Noise,” IEEE 13th International Conference on Data Mining Workshops, Dallas, 2013, pp. 1105–1112.
M. Kumar and A. Sharma, “Mining of data stream using DDenStream clustering algorithm,” IEEE International Conference in MOOC Innovation and Technology in Education (MITE), Jaipur, 2013, pp. 315–320.
W. Liu and J. OuYang, “Clustering Algorithm for High Dimensional Data Stream over Sliding Windows,” IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications, Changsha, 2011, pp. 1537–1542.
Qian Zhou, “A recent-biased clustering algorithm of data stream,” Second International Conference on Mechanic Automation and Control Engineering (MACE), Hohhot, 2011, pp. 3803–3808.
A. Zhou, F. Cao, Y. Yan, C. Sha and X. He, “Distributed Data Stream Clustering: A Fast EM-based Approach,” IEEE 23rd International Conference on Data Engineering, Istanbul, 2007, pp. 736–745.
R. Fathzadeh and V. Mokhtari, “An ensemble learning approach for data stream clustering,” 21st Iranian Conference on Electrical Engineering (ICEE), Mashhad, 2013, pp. 1–6.
M. m. Gao, J. z. Liu and X. x. Gao, “Application of Compound Gaussian Mixture Model clustering in the data stream,” International Conference on Computer Application and System Modeling (ICCASM 2010), Taiyuan, 2010, pp. V7-172-V7-177.
X. Zhang, C. Furtlehner, C. Germain-Renaud and M. Sebag, “Data Stream Clustering With Affinity Propagation,” IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 7, 2014, pp. 1644–1656.
H. Zhu, Y. Wang and Z. Yu, “Clustering of Evolving Data Stream with Multiple Adaptive Sliding Window,” Data Storage and Data Engineering (DSDE), International Conference on, Bangalore, 2010, pp. 95–100.
C. D. Wang, J. H. Lai, D. Huang and W. S. Zheng, “SVStream: A Support Vector-Based Algorithm for Clustering Data Streams,” IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 6, 2013, pp. 1410–1424.
Huanliang Sun, Ge Yu, Yubin Bao, Faxin Zhao and Daling Wang, “CDS-Tree: an effective index for clustering arbitrary shapes in data streams,” 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications (RIDE-SDMA’05), 2005, pp. 81–88.
Kehua Yang, HeqingGao, Lin Chen and Qiong Yuan, “Self-adaptive clustering data stream algorithm based on SSMC-tree,” 4th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, 2013, pp. 342–345.
Charu C. Aggarwal, “A Framework for Clustering Evolving Data Streams” Proceedings of the 29th VLDB Conference, Berlin, Germany, Vol. 29, 2003, pp. 81–92.
- Tweet Cluster Analyzer: Partition and Join-based Micro-clustering for Twitter Data Stream
M. Arun Manicka Raja
- Springer Singapore
Neuer Inhalt/© ITandMEDIA