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In this paper, we initiate a theoretical study of the problem of clustering data under interactive feedback. We introduce a query-based model in which users can provide feedback to...
Given the pairwise affinity relations associated with a set of data items, the goal of a clustering algorithm is to automatically partition the data into a small number of homogen...
We propose and test an objective criterion for evaluation of clustering performance: How well does a clustering algorithm run on unlabeled data aid a classification algorithm? The...
We propose a Bayesian approach to incorporate anatomical information in the clustering of fiber trajectories. An expectationmaximization (EM) algorithm is used to cluster the traj...
Clustering algorithms conduct a search through the space of possible organizations of a data set. In this paper, we propose two types of instance-level clustering constraints ? mu...
Supervised clustering is the problem of training a clustering algorithm to produce desirable clusterings: given sets of items and complete clusterings over these sets, we learn ho...
We describe a clustering algorithm based on continuous Hidden Markov Models (HMM) to automatically classify both electrocardiogram (ECG) and intracranial pressure (ICP) beats base...
We propose a variational bayes approach to the problem of robust estimation of gaussian mixtures from noisy input data. The proposed algorithm explicitly takes into account the un...