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2010 Ninth International Conference on Machine Learning and Applications, 2010
An algorithm is presented for clustering sequential data in which each unit is a collection of vectors. An example of such a type of data is speaker data in a speaker clustering problem. The algorithm first constructs affinity matrices between each pair of units, using a modified version of the Point Distribution algorithm which is initially developed ...
Jianfei Wu +5 more
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An algorithm is presented for clustering sequential data in which each unit is a collection of vectors. An example of such a type of data is speaker data in a speaker clustering problem. The algorithm first constructs affinity matrices between each pair of units, using a modified version of the Point Distribution algorithm which is initially developed ...
Jianfei Wu +5 more
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2010 4th International Universal Communication Symposium, 2010
Creating appropriate methods to facilitate the analysis of sequential data is a key issue in many applications. In this talk, we focus on the unique role of the parameter time in the context of visually driven data analysis. We will discuss three major aspects - visualization, analysis, and user interaction.
Yunhai Wang +4 more
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Creating appropriate methods to facilitate the analysis of sequential data is a key issue in many applications. In this talk, we focus on the unique role of the parameter time in the context of visually driven data analysis. We will discuss three major aspects - visualization, analysis, and user interaction.
Yunhai Wang +4 more
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Sequential data envelopment analysis
Annals of Operations Research, 2021zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rolf Färe, Valentin Zelenyuk
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K-Subspaces for Sequential Data
2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2023We study the problem of clustering high-dimensional temporal data such as video sequences of human motion, where points that arrive sequentially in time are likely to belong to the same cluster. State-of-the-art approaches to this problem rely on the union-of-subspaces model, where points lie near one of $K$ unknown low-dimensional subspaces.
Sheng, Wubin, Lipor, John
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Sequential Social Network Data
Psychometrika, 1988A new method is proposed for the statistical analysis of dyadic social interaction data measured over time. The data to be studied are assumed to be realizations of a social network of a fixed set of actors interacting on a single relation. The method is based on loglinear models for the probabilities for various dyad (or actor pair) states and ...
Wasserman, Stanley, Iacobucci, Dawn
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Rough clustering of sequential data
Data & Knowledge Engineering, 2007This paper presents a new indiscernibility-based rough agglomerative hierarchical clustering algorithm for sequential data. In this approach, the indiscernibility relation has been extended to a tolerance relation with the transitivity property being relaxed. Initial clusters are formed using a similarity upper approximation.
Pradeep Kumar 0001 +3 more
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Accelerated Sequential Data Clustering
Journal of ClassificationzbMATH Open Web Interface contents unavailable due to conflicting licenses.
Reza Mortazavi +2 more
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Proceedings of the 2016 International Conference on Management of Data, 2016
Errors are prevalent in data sequences, such as GPS trajectories or sensor readings. Existing methods on cleaning sequential data employ a constraint on value changing speeds and perform constraint-based repairing. While such speed constraints are effective in identifying large spike errors, the small errors that do not significantly deviate from the ...
Aoqian Zhang +2 more
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Errors are prevalent in data sequences, such as GPS trajectories or sensor readings. Existing methods on cleaning sequential data employ a constraint on value changing speeds and perform constraint-based repairing. While such speed constraints are effective in identifying large spike errors, the small errors that do not significantly deviate from the ...
Aoqian Zhang +2 more
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On optimal segmentation of sequential data
2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718), 2003We present an algorithm that efficiently computes optimal partitions of sequential data into 1 to N segments and propose a method to determine the most salient segmentation among them. As a by-product, we obtain a regularization parameter that can be used to compute such salient segmentations - also on new data sets - even more efficiently.
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