Comparison on membership functions in fuzzy k-member clustering for data anonymization

k-member clustering is an efficient method of k-anonymization, in which data samples are anonymized so that any sample is indistinguishable from at least k−1 other samples. Fuzzy k-member clustering is a fuzzy variant of k-member clustering, which extracts k-member clusters with fuzzy memberships of samples and makes it possible for the samples having large… (More)