A simple approach to sparse clustering

作者：Arias Castro Ery; Pu Xiao^*

来源：Computational Statistics & Data Analysis, 2017, 105: 217-228.

DOI：10.1016/j.csda.2016.08.003

• Sparse clustering
• Hill-climbing
• High-dimensional
• Feature selection

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.

• 出版日期2017-1