William F. Punch

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Clustering ensembles have emerged as a powerful method for improving both the robustness as well as the stability of unsupervised classification solutions. However, finding a consensus clustering from multiple partitions is a difficult problem that can be approached from graph-based, combinatorial, or statistical perspectives. This study extends previous(More)
Pattern recognition generally requires that objects be described in terms of a set of measurable features. The selection and quality of the features representing each pattern have a considerable bearing on the success of subsequent pattern classification. Feature extraction is the process of deriving new features from the original features in order to(More)
This paper presents an algorithm for recommending items using a diverse set of features. The items are recommended by performing a random walk on the k-partite graph constructed from the heterogenous features. To support personalized recommendation, the random walk must be initiated separately for each user, which is computationally demanding given the(More)
Clustering ensembles combine multiple partitions of the given data into a single clustering solution of better quality. Inspired by the success of supervised boosting algorithms, we devise an adaptive scheme for integration of multiple non-independent clusterings. Individual partitions in the ensemble are sequentially generated by clustering specially(More)