[PDF] Semi-Supervised Clustering with Partial Background Information
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Abstract. Incorporating background knowledge into unsupervised clustering algorithms has been the subject of extensive re- search in recent years.
By using a supervised or semi-supervised classification algorithm, the user looses the ability to understand his data and to discover new interesting patterns.
This paper forms this as a constrained optimization problem, and proposes two learning algorithms to solve the problem, based on hard and fuzzy clustering ...
In this paper we present a new algorithm for semi-supervised clustering. We assume to have a small set of labeled samples and we use it in a clustering ...
Incorporating background knowledge into unsupervised clustering algorithms has been the subject of extensive research in recent years.
Most of the existing semisupervised clustering algorithms can be divided into three categories: method based on labeled data [1][2][3][4][5][6][7][8][9], ...
[PDF] Semi-supervised Clustering with Limited Background Knowledge
www.cs.utexas.edu › semi-aaaidc-04
Our main goal in this thesis is to study constraint-based semi-supervised clustering algo- rithms, integrate them with metric-based approaches, char- acterize ...
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Mar 15, 2024 · We propose an unconstrained pre-processing method for the prior membership degree matrix by filling in missing values with an expert preference.
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In this paper we present a new algorithm for semi-super- vised clustering. We assume to have a small set of labeled samples and we use it in a clustering ...
Apr 8, 2016 · In this paper we present a new algorithm for semisupervised clustering. We assume to have a small set of labeled samples and we use it in a ...