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 …

Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and
methods for mining patterns, knowledge, and models from various kinds of data for diverse …

摘要半监督学习研究如何同时利用有类标签的样本和无类标签的样例改进学习性能,
成为近年来机器学习领域的研究热点. 鉴于半监督学习的理论意义和实际应用价值 …

Semi-supervised clustering algorithms aim to improve clustering results using limited
supervision. The supervision is generally given as pairwise constraints; such constraints are …

We explore the use of instance and cluster-level constraints with agglomerative hierarchical
clustering. Though previous work has illustrated the benefits of using constraints for non …

Visual analytics is the science of marrying interactive visualizations and analytic algorithms
to support exploratory knowledge discovery in large datasets. We argue for a shift from a …

Clustering with constraints is an active area of machine learning and data mining research.
Previous empirical work has convincingly shown that adding constraints to clustering …

Constrained Clustering allows to make the clustering task more accurate by integrating user
constraints, which can be instance-level or cluster-level constraints. Few works consider the …

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 …