Sequential clustering by statistical methodology

Amador, José J.

doi:10.1016/j.patrec.2005.04.003

Cited by 5 publications

(1 citation statement)

References 19 publications

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“…The objects in the same category present great similarity, while the objects in different categories present great phase specificity. Common clustering analysis methods include the sequential clustering algorithm [3], densitybased clustering algorithm [4,5] cost function optimization [6], boundary detection clustering algorithm, fuzzy clustering algorithm [7], minimum generative tree clustering [8,9], morphological transformation, and branch limit bound clustering algorithm [10] among others.…”

Section: Introductionmentioning

confidence: 99%

Extracting the columns of Gong-che musical score based on cluster analysis

Chen¹

2023

International Conference on Signal Processing and Communication Technology (SPCT 2022)

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In this study, the concept, main technology, and application scenarios of cluster analysis are briefly introduced for the Gong-che Notation sheet music, which was a widely used in ancient China. Various classification methods using different clustering algorithms are analyzed; accordingly, some application scenarios are discussed. This study focuses on extracting the columns from Chinese Gong-che Notation sheet music score images using systematic tree clustering. The experimental results show that different column’s number of Gong-che Notation sheet music score correspond to different extraction accuracy.

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