Compression-based data mining of sequential data
Article Properties
-
LanguageEnglish
-
DOI (url)
-
Publication Date2007/01/26
-
Indian UGC (journal)
-
Refrences50
-
Citations46
-
Eamonn Keogh
-
Stefano Lonardi
-
Chotirat Ann Ratanamahatana
-
Li Wei
-
Sang-Hee Lee
-
John Handley
Cite
Keogh, Eamonn, et al. “Compression-Based Data Mining of Sequential Data”. Data Mining and Knowledge Discovery, vol. 14, no. 1, 2007, pp. 99-129, https://doi.org/10.1007/s10618-006-0049-3.
Keogh, E., Lonardi, S., Ratanamahatana, C. A., Wei, L., Lee, S.-H., & Handley, J. (2007). Compression-based data mining of sequential data. Data Mining and Knowledge Discovery, 14(1), 99-129. https://doi.org/10.1007/s10618-006-0049-3
Keogh, Eamonn, Stefano Lonardi, Chotirat Ann Ratanamahatana, Li Wei, Sang-Hee Lee, and John Handley. “Compression-Based Data Mining of Sequential Data”. Data Mining and Knowledge Discovery 14, no. 1 (2007): 99-129. https://doi.org/10.1007/s10618-006-0049-3.
1.
Keogh E, Lonardi S, Ratanamahatana CA, Wei L, Lee SH, Handley J. Compression-based data mining of sequential data. Data Mining and Knowledge Discovery. 2007;14(1):99-129.
Journal Categories
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Science
Science (General)
Cybernetics
Information theory
Technology
Electrical engineering
Electronics
Nuclear engineering
Electronics
Technology
Mechanical engineering and machinery
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| 10.1161/01.CIR.101.23.e215 | Circulation |
| 2000 | |
| 10.1016/S0097-8485(00)80006-6 | 2000 | |||
| Graph-based data mining | IEEE Intelligent Systems and their Applications | 102 | 2000 | |
| 10.1023/A:1009752403260 | Data Mining and Knowledge Discovery |
| 1997 | |
| An Information Measure for Classification | The Computer Journal |
| 318 | 1968 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Enriching Representation and Enhancing Nearest Neighbor Classification of Slope/Landslide Data Using Rectified Feature Line Segments and Hypersphere-Based Scaling: A Reproducible Experimental Comparison | Mathematical Geosciences |
| 2023 | |
dCNN/dCAM: anomaly precursors discovery in multivariate time series with deep convolutional neural networks | Data-Centric Engineering |
| 2023 | |
| Classifying contaminated cell cultures using time series features | Journal of Applied Statistics |
| 2023 | |
GraphTS: Graph-represented time series for subsequence anomaly detection | PLOS ONE |
| 2 | 2023 |
Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series | Proceedings of the VLDB Endowment |
| 1 | 2023 |
Citations Analysis
The category
Science: Mathematics: Instruments and machines: Electronic computers. Computer science 25
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Krimp: mining itemsets that compress and was published in 2010. The most recent citation comes from a 2023 study titled dCNN/dCAM: anomaly precursors discovery in multivariate time series with deep convolutional neural networks. This article reached its peak citation in 2021, with 6 citations. It has been cited in 35 different journals, 17% of which are open access. Among related journals, the Proceedings of the VLDB Endowment cited this research the most, with 3 citations. The chart below illustrates the annual citation trends for this article.