Abstract Agglomerative Hierarchical Clustering (AHC) is a bottom-up clustering strategy in
which each object is originally a cluster, and more pairs of clusters are formed by traversing …

Clustering is a fundamental task in data analysis, aiming to partition a set of samples into
subsets called clusters based on their similarity. Agglomerative Hierarchical Clustering …

A semi-supervised agglomerative hierarchical clustering method based on dynamically
updating constraints is proposing in this research. Following the existing semi-supervised …

Recently, both ensemble clustering and semi-supervised clustering have emerged as
important paradigms of traditional clustering. Ensemble clustering seeks to integrate multiple …

Sažetak Hierarchical clustering is a common type of clustering in which the dataset is
hierarchically divided and represented by a dendrogram. Agglomerative Hierarchical …

Recently, semi-supervised clustering has been remarked and discussed in many
researches. In semi-supervised clustering, pairwise constraints, that is, must-link and cannot …

This paper presents a new semi-supervised agglomerative hierarchical clustering algorithm
with ward method using clusterwise tolerance. Recently, semi-supervised clustering has …

Abstract Agglomerative Hierarchical Clustering (AHC) is a general type of Hierarchical
Clustering (HC) that forms clusters from the “bottom-up.” This paper focuses on the …

This paper presents semi-supervised agglomerative hierarchical clustering algorithm using
clusterwise tolerance based pairwise constraints. In semi-supervised clustering, pairwise …

Semi-supervised clustering ensemble introduces partial supervised information, usually
pairwise constraints, to achieve better performance than clustering ensemble. Although it …