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  "Title": "Power Fuzzy Clustering and Cluster-Wise Regression",
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  "Authors@R": "c(person(given = \"Phuc Thinh\",\nfamily = \"Nguyen\",\nrole = c(\"aut\", \"cre\"),\nemail = \"phucthinh010603@yahoo.com\"),\nperson(given = \"Cristina\",\nfamily = \"Tortora\",\nrole = c(\"aut\", \"ths\", \"dgs\"),\nemail = \"cristina.tortora@sjsu.edu\"),\nperson(given = \"Antonio\",\nfamily = \"Punzo\",\nrole = c(\"aut\", \"ths\", \"dgs\"),\nemail = \"antonio.punzo@unict.it\"))",
  "Description": "Implementations of Power Fuzzy Clustering (PFC) and Power\nFuzzy Cluster-wise Regression (PFCR) for multivariate data. The\npackage supports Minkowski distances, with the L1 case solved\nvia iteratively re-weighted least squares and the case p > 1\nsolved via coordinate-wise root finding, as well as an\nadaptive, regularised Mahalanobis distance with per-cluster\ncovariance matrices. Both plain fuzzy clustering and\ncluster-wise linear regression are provided. The corresponding\npaper can be found at Nguyen P.T., Tortora C., and Punzo A.\n(2026) <doi:10.1109/TFUZZ.2026.3683998>.",
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  "Author": "Phuc Thinh Nguyen [aut, cre], Cristina Tortora [aut, ths, dgs],\nAntonio Punzo [aut, ths, dgs]",
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