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  "Title": "Proximity Measure Based Diagnostics for Standard, Soft, and\nMulti-Way Clustering",
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  "Language": "en-US",
  "Authors@R": "c(person(given = \"Shrikrishna\",\nfamily = \"Bhat K\",\nrole = c(\"aut\", \"cre\", \"cph\"),\nemail = \"skbhat.in@gmail.com\",\ncomment = c(ORCID = \"0009-0000-6180-5783\")),\nperson(given = \"Kiruthika\",\nfamily = \"C\",\nrole = \"aut\",\ncomment = c(ORCID = \"0000-0001-9655-702X\")))",
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  "Description": "Quantifies clustering quality by measuring both cohesion\nwithin clusters and separation between clusters. Implements\nadvanced silhouette width computations for diverse clustering\nstructures, including: simplified silhouette (Van der Laan et\nal., 2003) <doi:10.1080/0094965031000136012>, Probability of\nAlternative Cluster normalization methods (Raymaekers &\nRousseeuw, 2022) <doi:10.1080/10618600.2022.2050249>, fuzzy\nclustering and silhouette diagnostics using membership\nprobabilities (Campello & Hruschka, 2006; Menardi, 2011; Bhat &\nKiruthika, 2024) <doi:10.1016/j.fss.2006.07.006>,\n<doi:10.1007/s11222-010-9169-0>,\n<doi:10.1080/23737484.2024.2408534>, and multi-way clustering\nextensions such as block and tensor clustering (Schepers et\nal., 2008; Bhat & Kiruthika, 2025)\n<doi:10.1007/s00357-008-9005-9>,\n<doi:10.21203/rs.3.rs-6973596/v1>. Provides tools for\ncomputation and visualization (Rousseeuw, 1987)\n<doi:10.1016/0377-0427(87)90125-7> to support robust and\nreproducible cluster diagnostics across standard, soft, and\nmulti-way clustering settings.",
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        "Silhouette",
        "summary.Silhouette"
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    },
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