Help:Editing: Difference between revisions

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{Cleanup|date=October 2010|reason= Just a thought: how relevant is this discussion "Dimensionality reduction techniques" to the concept of "subspace clustering". As in subspace clustering, the goal is to find a set of features (relevant features, the concept is referred to as local feature relevance in the literature) in the high dimensional space, where potential subspaces accommodating different classes of data points can be defined. This means; the data points are dense when they are considered in a subset of dimensions (features). }


{Cleanup}

Latest revision as of 18:49, 13 October 2010

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