Abstract Semi-supervised clustering (SSC), a technique integrating semi-supervised
learning and clustering analysis, incorporates the given prior information (eg, class labels …

Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is
useful in a wide variety of applications, including document processing and modern …

Traditional cluster ensemble approaches have three limitations:() They do not make use of
prior knowledge of the datasets given by experts.() Most of the conventional cluster …

Semi-supervised clustering is a new learning method which combines semi-supervised
learning (SSL) and cluster analysis. It is widely valued and applied to machine learning …

The most well-known unsupervised classification algorithms allow for the identification of
hard or probabilistic partitions. However, when working with complex datasets such as those …

Semi-supervised clustering leverages prior information in the form of constraints to achieve
higher-quality clustering outcomes. However, most existing methods struggle with large …

Infrared (IR) spectroscopy has greatly improved the ability to study biomedical samples
because IR spectroscopy measures how molecules interact with infrared light, providing a …

Conventional semi-supervised clustering approaches have several shortcomings, such as
(1) not fully utilizing all useful must-link and cannot-link constraints,(2) not considering how …

Abstract In Machine Learning, the datasets used to build models are one of the main factors
limiting what these models can achieve and how good their predictive performance is …

We introduce a framework for the optimal extraction of flat clusterings from local cuts through
cluster hierarchies. The extraction of a flat clustering from a cluster tree is formulated as an …