In the first step feature terms are clustered using a little supervised information. In the second step, the feature terms are merged as new feature attributes.
This paper proposes an AHC-based ensemble semi-supervised clustering algorithm to improve performance.
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A semi-supervised clustering algorithm is proposed for hierarchical co-clustering, where feature terms are clustered using a little supervised information ...
Clustering methods that can be applied to partially labeled data or data with other types of outcome measures are known as semi-supervised clustering methods ( ...
In the first step feature terms are clustered using a little supervised information. In the second step, the feature terms are merged as new feature attributes.
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We propose a Semi-supervised Hierarchical Ensemble Clustering framework based on a novel Similarity metric and stratified feature Sampling, which we call ...
Sep 1, 2023 · Semi-supervised clustering involves using a small amount of class membership information in some samples for the learning process. Meanwhile, ...
In this paper a semi-supervised cluster- ing algorithm is proposed for hierarchical co-clustering. In the first step feature terms are clustered using a little ...
Inspired by the “compact-cluster” assumption, we propose a density-based semi-supervised hierarchical clustering (DBSSHC) method which iteratively splits ...
Jan 16, 2015 · In genomics, hierarchical clustering (HC) is a popular method for grouping similar samples based on a distance measure.