IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
A PCA-based similarity measure for multivariate time series
Proceedings of the 2nd ACM international workshop on Multimedia databases
An empirical study of the robustness of two module clustering fitness functions
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Feature Subset Selection and Feature Ranking for Multivariate Time Series
IEEE Transactions on Knowledge and Data Engineering
On the Stationarity of Multivariate Time Series for Correlation-Based Data Analysis
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
RGFGA: An Efficient Representation and Crossover for Grouping Genetic Algorithms
Evolutionary Computation
Clustering the heap in multi-threaded applications for improved garbage collection
Proceedings of the 8th annual conference on Genetic and evolutionary computation
An efficient k nearest neighbor search for multivariate time series
Information and Computation
A clustering procedure for exploratory mining of vector time series
Pattern Recognition
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolutionary algorithms for grouping high dimensional Email data
Intelligent Data Analysis
On the recording reference contribution to EEG correlation, phase synchorony, and coherence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
CLeVer: a feature subset selection technique for multivariate time series
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Survey: A survey on search-based software design
Computer Science Review
An analysis of the effects of composite objectives in multiobjective software module clustering
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Evaluating the importance of randomness in search-based software engineering
SSBSE'12 Proceedings of the 4th international conference on Search Based Software Engineering
International Journal of Computer Applications in Technology
Hi-index | 0.00 |
The decomposition of high-dimensional multivariate time series (MTS) into a number of low-dimensional MTS is a useful but challenging task because the number of possible dependencies between variables is likely to be huge. This paper is about a systematic study of the “variable groupings” problem in MTS. In particular, we investigate different methods of utilizing the information regarding correlations among MTS variables. This type of method does not appear to have been studied before. In all, 15 methods are suggested and applied to six datasets where there are identifiable mixed groupings of MTS variables. This paper describes the general methodology, reports extensive experimental results, and concludes with useful insights on the strength and weakness of this type of grouping method