CAFE rescales input data with mwk-means feature weights to reverse within-cluster dispersion order, suppressing noisy features and improving downstream unsupervised feature extraction under controlled noise.
A comprehensive survey of clustering al- gorithms: State-of-the-art machine learning applications, taxonomy, chal- lenges, and future research prospects
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Cluster-Adaptive Feature Extraction and its Theoretical Foundation with Minkowski Weighted k-Means
CAFE rescales input data with mwk-means feature weights to reverse within-cluster dispersion order, suppressing noisy features and improving downstream unsupervised feature extraction under controlled noise.