By Raupp F.

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Extra resources for A Center Cutting Plane Algorithm for a Likelihood Estimate Problem

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When the user selects a subset of the clusters for further examination, the system gathers their components and regroups them to form new clusters. Scatter/Gather aims at pursuing and finding structure in a small part of a corpus. This makes it an interesting complement to our approach: Scatter/Gather may provide an effective means for browsing and focusing on clusters of interest, and semi-supervised learning may be an effective means of improving the quality of those clusters. Note that we do not compare our performance to that of other purely unsupervised clustering systems such as AutoClass [3], COBWEB [9], or Iterative Optimization [10].

They propose a discriminatory learning with constraints problem that falls under the rubric of regularized empirical risk minimization. They provide non-convex and convex loss functions that make use of constraints and derive several algorithms for these loss functions such as logistic regression and support vector machines. They provide a striking example of using constraints in streaming video by illustrating that automatically generated constraints can be easily created from the data in the absence of human labeling.

1b shows the result of clustering with two must-link constraints between instances with similar heights, and one cannot-link constraint between two individuals with different heights. A drawback of the cop-kmeans approach is that it may fail to find a satisfying solution even when one exists. This happens because of the greedy fashion in which items are assigned; early assignments can constrain later ones due to potential conflicts, and there is no mechanism for backtracking. 1: Illustrative example: Clustering (k = 2) with hard pairwise constraints.

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