An efficient k nearest neighbor search for multivariate time series
Information and Computation
Detecting outlier samples in multivariate time series dataset
Knowledge-Based Systems
Indexing 3-D human motion repositories for content-based retrieval
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Clustering of human motions based on feature-level fusion of multiple body sensor data
Proceedings of the 1st ACM International Health Informatics Symposium
A review on time series data mining
Engineering Applications of Artificial Intelligence
Knowledge discovery from 3D human motion streams through semantic dimensional reduction
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Hierarchical indexing structure for 3d human motions
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Analyzing and Visualizing Jump Performance Using Wireless Body Sensors
ACM Transactions on Embedded Computing Systems (TECS) - Special Section on CAPA'09, Special Section on WHS'09, and Special Section VCPSS' 09
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Multivariate time series (MTS) datasets are common in various multimedia, medical and financial applications. In previous work, we introduced a similarity measure for MTS datasets, termed Eros (Extended Frobenius norm), which is based on the Frobenius Norm and Principal Component Analysis (PCA). Eros computes the similarity between two MTS items by measuring how close the corresponding principal components (PCs) are using the eigenvalues as weights. Since the weights are based on the data items in the database, they change whenever data are inserted into or removed from the database. In this paper, we propose a distance-based index structure, Muse (Multilevel distance-based index structure for Eros), for efficient retrieval of MTS items using Eros. Muse constructs each level as a distance-based index structure without using the weights, up to z levels. At the query time, Muse combines the z levels with the weights, which enables the weights to change without the need to rebuild the index structure. In order to show the efficiency of Muse, we performed several experiments on a set of synthetically generated clustered datasets. The results show the superiority of Muse as compared to Sequential Scan and M-tree in performance.