Apr 19, 2017 · In this paper, we first propose the transitive closure based constraint propagation approach, which makes use of the transitive closure operator ...
Section IV presents a random sub- space based semi-supervised clustering ensemble framework. Section V introduces an adaptive semi-supervised clustering.
Semi-supervised clustering,, is an important sub-field of clustering and is widely applied in different areas, such as image processing,, multimedia, pattern ...
Feb 15, 2024 · Yu et al. [39] proposed an adaptive semi-supervised clustering ensemble framework on high-dimensional data, which adopts random subspace ...
Adaptive Ensembling of Semi-Supervised Clustering Solutions
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Conventional semi-supervised clustering approaches have several shortcomings, such as (1) not fully utilizing all useful must-link and cannot-link ...
Conventional semi-supervised clustering approaches have several shortcomings, such as (1) not fully utilizing all useful must-link and cannot-link ...
This paper proposes an AHC-based ensemble semi-supervised clustering algorithm to improve performance.
Adaptive Ensembling of Semi-Supervised Clustering Solutions ...
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Title. Adaptive Ensembling of Semi-Supervised Clustering Solutions. Authors. Yu, Zhiwen; Kuang, Zongqiang; Liu, Jiming; Chen, Hongsheng; Zhang, Jun; You, ...
Adaptive Ensembling of Semi-Supervised Clustering Solutions. Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal.
Although semi-supervised clustering ensemble methods have achieved satisfactory performance, they fail to effectively utilize the constrained knowledge such ...