Active Informative Pairwise Constraint Formulation Algorithm for Constraint-Based Clustering

Clustering Formulation Using Constraint Optimization

The problem of clustering a set of data is a textbook machine learning problem, but at the same time, at heart, a typical optimization problem. Given an objective function, such as minimizing the intra-cluster distances or maximizing the inter-cluster distances, the task is to find an assignment of data points to clusters that achieves this objective. In this paper, we present a constraint prog...

Active Learning Method for Constraint-Based Clustering Algorithms

Clustering Algorithm for Network Constraint Trajectories

Spatial data mining is an active topic in spatial databases. This paper proposes a new clustering method for moving object trajectories databases. It applies specifically to trajectories that only lie on a predefined network. The proposed algorithm (NETSCAN) is inspired from the wellknown density based algorithms. However, it takes advantage of the network constraint to estimate the object dens...

Early Active Learning with Pairwise Constraint for Person Re-identification

Research on person re-identification (re-id) has attached much attention in the machine learning field in recent years. With sufficient labeled training data, supervised re-id algorithm can obtain promising performance. However, producing labeled data for training supervised reid models is an extremely challenging and time-consuming task because it requires every pair of images across no-overla...

Integrated constraint based clustering algorithm for high dimensional data

Dimension selection, dimension weighting and data assignment are three circular dependent essential tasks for high dimensional data clustering and each such task is challenging. To meet the challenge of high dimensional data clustering, constraints have been employed in several previous works. However, these constraint based algorithms use constraints to help accomplish only one of the three es...