Semi-supervised or constrained graph clustering incorporates prior information in order to improve results. Pairwise constraints are often utilized guide the process. This work addresses a problem biological networks where (1) subgraph connectivity strictly required be satisfied and (2) quality is assessed with respect pairwise constraint violations. Existing methods fail fully satisfy constrai...

In this paper, the attributes employed to model the constraints are called constraint attributes and those attributes involved in the objective function to be optimized are called cost-optimal attributes. The constrained clustering considered is conducted in such a way that the objective function of cost-optimal attributes is optimized subject to the condition that the imposed constraint is sat...

When using deep neural networks in medical image classification tasks, it is mandatory to prepare a large-scale labeled set, and this often requires significant effort by experts. One strategy reduce the labeling cost group-based labeling, where samples are clustered then label attached each cluster. The efficiency of depends on purity clusters. Constrained clustering an effective way improve c...

Title of dissertation: Semi-supervised and Active Image Clustering with Pairwise Constraints from Humans Arijit Biswas, Doctor of Philosophy, 2014 Dissertation directed by: Prof. David W. Jacobs Department of Computer Science University of Maryland, College Park Clustering images has been an interesting problem for computer vision and machine learning researchers for many years. However as the ...

We present a simple spectral approach to the well-studied constrained clustering problem. It captures constrained clustering as a generalized eigenvalue problem with graph Laplacians. The algorithm works in nearly-linear time and provides concrete guarantees for the quality of the clusters, at least for the case of 2-way partitioning. In practice this translates to a very fast implementation th...

In recent years there has emerged the field of Constrained Clustering, which proposes clustering algorithms which are able to accommodate domain information to obtain a better final grouping. This information is usually provided as pairwise constraints, whose acquisition from humans can be costly. In this paper we propose a novel method based on word n-grams to automatically extract positive co...

Hierarchical data representations in the context of classification and data clustering were put forward during the fifties. Recently, hierarchical image representations have gained renewed interest for segmentation purposes. In this paper, we briefly survey fundamental results on hierarchical clustering and then detail recent paradigms developed for the hierarchical representation of images in ...