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 …

Distance metric is a key issue in many machine learning algorithms. This paper considers a
general problem of learning from pairwise constraints in the form of must-links and cannot …

Finite mixture models have a long history in statistics, having been used to model population
heterogeneity, generalize distributional assumptions, and lately, for providing a convenient …

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

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 …

This work studies the semantic segmentation of 3D LiDAR data in dynamic scenes for
autonomous driving applications. A system of semantic segmentation using 3D LiDAR data …

Advances in Machine Learning and Data Mining for Astronomy Page 1 W ay, Scargle, Chapman
& Hall/CRC Data Mining and Knowledge Discovery Series Advances in Machine Learning …

Most existing representative works in semi-supervised clustering do not sufficiently solve the
violation problem of pairwise constraints. On the other hand, traditional kernel methods for …

Despite the promising progress having been made, the two challenges of multi-view
clustering (MVC) are still waiting for better solutions: i) Most existing methods are either not …