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.
Biclustering in bioinformat- ics using big data and high performance computing applications: challenges and perspectives, a review: A. lopez-fernandez et al
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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.