Dong et al., 2025 - Google Patents
Interpretable sequence clusteringDong et al., 2025
View PDF- Document ID
- 5198301641507625258
- Author
- Dong J
- Yang X
- Jiang M
- Hu L
- He Z
- Publication year
- Publication venue
- Information Sciences
External Links
Snippet
Categorical sequence clustering is vital across various domains; however, the interpretability of cluster assignments presents considerable challenges. Sequences inherently lack explicit features, and existing sequence clustering algorithms heavily rely on …
- 238000000034 method 0 abstract description 163
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- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
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- G—PHYSICS
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- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
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- G06K9/6256—Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
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