Article ID Journal Published Year Pages File Type
6868919 Computational Statistics & Data Analysis 2017 12 Pages PDF
Abstract
Consider the problem of sparse clustering, where it is assumed that only a subset of the features are useful for clustering purposes. In the framework of the COSA method of Friedman and Meulman, subsequently improved in the form of the Sparse K-means method of Witten and Tibshirani, a natural and simpler hill-climbing approach is introduced. The new method is shown to be competitive with these two methods and others.
Related Topics
Physical Sciences and Engineering Computer Science Computational Theory and Mathematics
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