Open Access
| Issue |
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
|
|
|---|---|---|
| Article Number | 02049 | |
| Number of page(s) | 7 | |
| Section | 3D Images Reconstruction and Virtual System | |
| DOI | https://doi.org/10.1051/matecconf/201823202049 | |
| Published online | 19 November 2018 | |
- Mooney, Carl H., and J. F. Roddick. “Sequential pattern mining -approaches and algorithms.” Acm Computing Surveys (2013). [Google Scholar]
- Wijk, Jarke J. Van, and E. R. V. Selow. “Cluster and Calendar Based Visualization of Time Series Data.” IEEE Symposium on Information Visualization IEEE (2002). [Google Scholar]
- K. Swamy, G. Babu, R. Venkatasubbaih,“Identification of Frequent Item Search Patterns Using Apriori Algorithm and Weka Tool”, International Journal of Innovative Technology and Research:2401-2403 (2015). [Google Scholar]
- Yan, X. “CloSpan : Mining closed sequential patterns in large datasets.” Siam International Conference on Data Mining:166-177 (2003). [Google Scholar]
- Gyenesei, Attila, et al. “Mining co-regulated gene profiles for the detection of functional associations in gene expression data.” Bioinformatics:1927-1935 (2007). [CrossRef] [Google Scholar]
- Király, András, et al. “Novel techniques and an efficient algorithm for closed pattern mining.” Expert Systems with Applications:5105-5114 (2014). [CrossRef] [Google Scholar]
- Chen, Hsinchun, et al. “Visualization in law enforcement.” CHI ’05 Extended Abstracts on Human Factors in Computing Systems ACM:1268-1271 (2005). [CrossRef] [Google Scholar]
- Siripatana, Adil, et al. “The development of interactive 3D spring visualization for periodic multidimensional direction time-series data sets.” International Conference on Electrical Engineering/electronics, Computer, Telecommunications and Information Technology IEEE:1-4 (2012). [Google Scholar]
- Patel, Pranav, et al. “Mining Motifs in Massive Time Series Databases.” IEEE International Conference on Data Mining (2002). [Google Scholar]
- Yuan Li, Jessica Lin, and Tim Oates. “Visualizing variable-length time series motifs.” Proceedings of the 2012 SIAM International Conference on Data Mining:895-906 (2012). [Google Scholar]
- Hao, Ming C., et al. “Visual exploration of frequent patterns in multivariate time series.” Information Visualization:71-83 (2012). [CrossRef] [Google Scholar]
- Cao, N., et al. “Voila: Visual Anomaly Detection and Monitoring with Streaming Spatiotemporal Data. “ IEEE Transactions on Visualization & Computer Graphics:23-33 (2017). [Google Scholar]
- Wu, W., et al. “TelCoVis: Visual Exploration of Co-occurrence in Urban Human Mobility Based on Telco Data. “ IEEE Transactions on Visualization & Computer Graphics:935-944 (2015). [Google Scholar]
- Chen, Y., P. Xu, and L. Ren. “Sequence Synopsis: Optimize Visual Summary of Temporal Event Data. “ IEEE Transactions on Visualization & Computer Graphics :1-1 (2017). [Google Scholar]
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