{
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  "Package": "FCPS",
  "Type": "Package",
  "Title": "Fundamental Clustering Problems Suite",
  "Version": "1.4.1",
  "Date": "2026-07-23",
  "Authors@R": "c(person(\"Michael\", \"Thrun\", email= \"m.thrun@gmx.net\",role=c(\"aut\",\"cre\",\"cph\"), comment = c(ORCID = \"0000-0001-9542-5543\")),person(\"Peter\", \"Nahrgang\",role=c(\"ctr\",\"ctb\")),person(\"Felix\", \"Pape\",role=c(\"ctr\",\"ctb\")),person(\"Vasyl\",\"Pihur\", role=c(\"ctb\")),person(\"Guy\",\"Brock\", role=c(\"ctb\")),person(\"Susmita\",\"Datta\", role=c(\"ctb\")),person(\"Somnath\",\"Datta\", role=c(\"ctb\")),person(\"Luis\",\"Winckelmann\", role=c(\"com\")),person(\"Alfred\", \"Ultsch\",role=c(\"dtc\",\"ctb\")),person(\"Quirin\", \"Stier\",role=c(\"ctb\",\"rev\")))",
  "Maintainer": "Michael Thrun <m.thrun@gmx.net>",
  "Description": "Over sixty clustering algorithms are provided in this\npackage with consistent input and output, which enables the\nuser to try out algorithms swiftly. Additionally, 26\nstatistical approaches for the estimation of the number of\nclusters as well as the mirrored density plot (MD-plot) of\nclusterability are implemented. The packages is published in\nThrun, M.C., Stier Q.: \"Fundamental Clustering Algorithms\nSuite\" (2021), SoftwareX, <DOI:10.1016/j.softx.2020.100642>.\nMoreover, the fundamental clustering problems suite (FCPS)\noffers a variety of clustering challenges any algorithm should\nhandle when facing real world data, see Thrun, M.C., Ultsch A.:\n\"Clustering Benchmark Datasets Exploiting the Fundamental\nClustering Problems\" (2020), Data in Brief,\n<DOI:10.1016/j.dib.2020.105501>.",
  "License": "GPL-3",
  "LazyData": "TRUE",
  "LazyLoad": "yes",
  "URL": "https://www.deepbionics.org/",
  "BugReports": "https://github.com/Mthrun/FCPS/issues",
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  "Repository": "https://mthrun.r-universe.dev",
  "Date/Publication": "2026-07-23 07:08:48 UTC",
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  "Packaged": {
    "Date": "2026-07-23 11:09:33 UTC",
    "User": "root"
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  "Author": "Michael Thrun [aut, cre, cph] (ORCID:\n<https://orcid.org/0000-0001-9542-5543>),\nPeter Nahrgang [ctr, ctb],\nFelix Pape [ctr, ctb],\nVasyl Pihur [ctb],\nGuy Brock [ctb],\nSusmita Datta [ctb],\nSomnath Datta [ctb],\nLuis Winckelmann [com],\nAlfred Ultsch [dtc, ctb],\nQuirin Stier [ctb, rev]",
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  "_created": "2026-07-23T11:09:33.000Z",
  "_published": "2026-07-23T11:38:27.271Z",
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      "title": "Atom introduced in [Ultsch, 2004].",
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      "page": "FCPS-package",
      "title": "Fundamental Clustering Problems Suite",
      "concept": [
        "data set",
        "Fundamental Clustering Problems Suite"
      ],
      "topics": [
        "FCPS-package",
        "ClusteringAlgorithms"
      ]
    },
    {
      "page": "ADPclustering",
      "title": "(Adaptive) Density Peak Clustering algorithm using automatic parameter selection",
      "concept": [
        "fast search and find of density peaks"
      ],
      "topics": [
        "ADPclustering"
      ]
    },
    {
      "page": "AgglomerativeNestingClustering",
      "title": "AGNES clustering",
      "concept": [
        "Agglomerative Nestingg"
      ],
      "topics": [
        "AgglomerativeNestingClustering"
      ]
    },
    {
      "page": "APclustering",
      "title": "Affinity Propagation Clustering",
      "concept": [
        "Affinity Propagation"
      ],
      "topics": [
        "APclustering"
      ]
    },
    {
      "page": "Atom",
      "title": "Atom introduced in [Ultsch, 2004].",
      "topics": [
        "Atom"
      ]
    },
    {
      "page": "AutomaticProjectionBasedClustering",
      "title": "Automatic Projection-Based Clustering",
      "concept": [
        "Projection Based Clustering"
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        "AutomaticProjectionBasedClustering"
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    },
    {
      "page": "Chainlink",
      "title": "Chainlink introduced in [Ultsch et al., 1994; Ultsch, 1995].",
      "topics": [
        "Chainlink"
      ]
    },
    {
      "page": "ClusterabilityMDplot",
      "title": "Clusterability MDplot",
      "topics": [
        "ClusterabilityMDplot"
      ]
    },
    {
      "page": "ClusterAMI",
      "title": "Adjusted Mutual Information [Vinh et al., 2009]",
      "concept": [
        "adjusted mutual information",
        "mutual information"
      ],
      "topics": [
        "ClusterAMI"
      ]
    },
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      "title": "Applies a function over grouped data",
      "topics": [
        "ClusterApply"
      ]
    },
    {
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      "title": "Adjusted Rand index",
      "concept": [
        "adjusted rand index"
      ],
      "topics": [
        "ClusterARI"
      ]
    },
    {
      "page": "ClusterChallenge",
      "title": "Generates a Fundamental Clustering Challenge based on specific artificial datasets.",
      "concept": [
        "Generate Fundamental Clustering Problem",
        "Cluster Challenge"
      ],
      "topics": [
        "ClusterChallenge"
      ]
    },
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      "title": "ClusterCount",
      "concept": [
        "Cluster Count"
      ],
      "topics": [
        "ClusterCount"
      ]
    },
    {
      "page": "ClusterCreateClassification",
      "title": "Create Classification for Cluster.. functions",
      "concept": [
        "Create Cluster Classification"
      ],
      "topics": [
        "ClusterCreateClassification"
      ]
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    {
      "page": "ClusterDaviesBouldinIndex",
      "title": "Davies Bouldin Index",
      "concept": [
        "Davies Bouldin Index"
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      "topics": [
        "ClusterDaviesBouldinIndex"
      ]
    },
    {
      "page": "ClusterDendrogram",
      "title": "Cluster Dendrogram",
      "concept": [
        "Cluster Dendrogram"
      ],
      "topics": [
        "ClusterDendrogram"
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    },
    {
      "page": "ClusterDistances",
      "title": "ClusterDistances",
      "concept": [
        "intra cluster"
      ],
      "topics": [
        "ClusterDistances",
        "ClusterIntraDistances",
        "IntraClusterDistances"
      ]
    },
    {
      "page": "ClusterDunnIndex",
      "title": "Dunn Index",
      "concept": [
        "Dunn Index"
      ],
      "topics": [
        "ClusterDunnIndex"
      ]
    },
    {
      "page": "ClusterEqualWeighting",
      "title": "ClusterEqualWeighting",
      "concept": [
        "Equal Weighting",
        "Cluster Sampling"
      ],
      "topics": [
        "ClusterEqualWeighting"
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    },
    {
      "page": "ClusterAccuracy",
      "title": "ClusterAccuracy",
      "topics": [
        "ClusterAccuracy"
      ]
    },
    {
      "page": "ClusterInterDistances",
      "title": "Computes Inter-Cluster Distances",
      "concept": [
        "inter cluster"
      ],
      "topics": [
        "ClusterInterDistances",
        "InterClusterDistances"
      ]
    },
    {
      "page": "ClusterMCC",
      "title": "Matthews Correlation Coefficient (MCC)",
      "concept": [
        "Matthews Correlation Coefficient",
        "Matthews Correlation",
        "Rk statistic"
      ],
      "topics": [
        "ClusterMCC"
      ]
    },
    {
      "page": "ClusterNMI",
      "title": "Normalized Mutual Information [Strehl et al., 2003]",
      "concept": [
        "normalized mutual information",
        "mutual information"
      ],
      "topics": [
        "ClusterNMI"
      ]
    },
    {
      "page": "ClusterNoEstimation",
      "title": "Estimates Number of Clusters using up to 26 Indicators",
      "concept": [
        "Estimation of Number of Clusters"
      ],
      "topics": [
        "ClusterNoEstimation"
      ]
    },
    {
      "page": "ClusterNormalize",
      "title": "Cluster Normalize",
      "concept": [
        "Consecutive Clustering",
        "Cluster Normalize"
      ],
      "topics": [
        "ClusterNormalize"
      ]
    },
    {
      "page": "ClusterPlotMDS",
      "title": "Plot Clustering using Dimensionality Reduction by MDS",
      "concept": [
        "Multidimensional scaling",
        "Projection Method"
      ],
      "topics": [
        "ClusterPlotMDS"
      ]
    },
    {
      "page": "ClusterRedefine",
      "title": "Redfines Clustering",
      "topics": [
        "ClusterRedefine"
      ]
    },
    {
      "page": "ClusterRename",
      "title": "Renames Clustering",
      "topics": [
        "ClusterRename"
      ]
    },
    {
      "page": "ClusterRenameDescendingSize",
      "title": "Cluster Rename Descending Size",
      "concept": [
        "Descending Clustering",
        "Rename Descending Cluster Size"
      ],
      "topics": [
        "ClusterRenameDescendingSize"
      ]
    },
    {
      "page": "ClusterShannonInfo",
      "title": "Shannon Information",
      "concept": [
        "Shannon information"
      ],
      "topics": [
        "ClusterShannonInfo"
      ]
    },
    {
      "page": "ClusterUpsamplingMinority",
      "title": "Cluster Up Sampling using SMOTE for minority cluster",
      "concept": [
        "up sampling",
        "over sampling"
      ],
      "topics": [
        "ClusterUpsamplingMinority"
      ]
    },
    {
      "page": "ConsensusClustering",
      "title": "Consensus Clustering",
      "topics": [
        "ConsensusClustering"
      ]
    },
    {
      "page": "CrossEntropyClustering",
      "title": "Cross-Entropy Clustering",
      "concept": [
        "Cross-Entropy Clustering",
        "Cross-Entropy"
      ],
      "topics": [
        "CrossEntropyClustering"
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    },
    {
      "page": "DBscan",
      "title": "DBSCAN",
      "topics": [
        "DBSCAN",
        "DBscan"
      ]
    },
    {
      "page": "DatabionicSwarmClustering",
      "title": "Databionic Swarm (DBS) Clustering and Visualization",
      "concept": [
        "Databionic swarm",
        "generalized Umatrix",
        "cluster analysis"
      ],
      "topics": [
        "DatabionicSwarmClustering",
        "DBSclusteringAndVisualization"
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    },
    {
      "page": "DensityPeakClustering",
      "title": "Density Peak Clustering algorithm using the Decision Graph",
      "concept": [
        "Density Peak Clustering",
        "Density Peak"
      ],
      "topics": [
        "DensityPeakClustering"
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    },
    {
      "page": "dietary_survey_IBS",
      "title": "Dietary survey IBS [Hayes et al., 2013]",
      "topics": [
        "dietary_survey_IBS"
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    },
    {
      "page": "DivisiveAnalysisClustering",
      "title": "Large DivisiveAnalysisClustering Clustering",
      "concept": [
        "Divisive Analysis Clustering"
      ],
      "topics": [
        "DivisiveAnalysisClustering"
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    },
    {
      "page": "EngyTime",
      "title": "EngyTime introduced in [Baggenstoss, 2002].",
      "topics": [
        "EngyTime"
      ]
    },
    {
      "page": "EntropyOfDataField",
      "title": "Entropy Of a Data Field [Wang et al., 2011].",
      "concept": [
        "data field",
        "data entropy"
      ],
      "topics": [
        "EntropyOfDataField"
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    },
    {
      "page": "EstimateRadiusByDistance",
      "title": "Estimate Radius By Distance",
      "topics": [
        "EstimateRadiusByDistance"
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    },
    {
      "page": "FannyClustering",
      "title": "Fuzzy Analysis Clustering [Rousseeuw/Kaufman, 1990, p. 253-279]",
      "concept": [
        "fuzzy clustering"
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      "topics": [
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    {
      "page": "GapStatistic",
      "title": "Gap Statistic",
      "concept": [
        "Gap Statistic"
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      "topics": [
        "GapStatistic"
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    },
    {
      "page": "GenieClustering",
      "title": "Genie Clustering by Gini Index",
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    },
    {
      "page": "GolfBall",
      "title": "GolfBall introduced in [Ultsch, 2005]",
      "topics": [
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    },
    {
      "page": "HCLclustering",
      "title": "On-line Update (Hard Competitive learning) method",
      "concept": [
        "Hard Competitive learning clustering"
      ],
      "topics": [
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      ]
    },
    {
      "page": "HDDClustering",
      "title": "HDD clustering is a model-based clustering method of [Bouveyron et al., 2007].",
      "concept": [
        "model-based clustering"
      ],
      "topics": [
        "HDDClustering"
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    },
    {
      "page": "Hepta",
      "title": "Hepta introduced in [Ultsch, 2003]",
      "topics": [
        "Hepta"
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    },
    {
      "page": "HierarchicalClusterData",
      "title": "Internal function of Hierarchical Clusterering of Data",
      "topics": [
        "HierarchicalCluster",
        "HierarchicalClusterData"
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    },
    {
      "page": "HierarchicalClusterDists",
      "title": "Internal Function of Hierarchical Clustering with Distances",
      "topics": [
        "HierarchicalClusterDists"
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    },
    {
      "page": "HierarchicalClustering",
      "title": "Hierarchical Clustering",
      "concept": [
        "Hierarchical Clustering"
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      "topics": [
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    },
    {
      "page": "HierarchicalDBSCAN",
      "title": "Hierarchical DBSCAN",
      "concept": [
        "Hierarchical DBSCAN"
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      "topics": [
        "HierarchicalDBSCAN",
        "Hierarchical_DBSCAN",
        "Hierarchical_DBscan"
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    {
      "page": "kmeansClustering",
      "title": "K-Means Clustering",
      "concept": [
        "kmeans Clustering"
      ],
      "topics": [
        "kmeansClustering"
      ]
    },
    {
      "page": "kmeansdist",
      "title": "k-means Clustering using a distance matrix",
      "concept": [
        "kmeans Clustering"
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      "topics": [
        "kmeansDist"
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    },
    {
      "page": "LargeApplicationClustering",
      "title": "Large Application Clustering",
      "concept": [
        "Large Application Clusteringg"
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      "topics": [
        "LargeApplicationClustering"
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    },
    {
      "page": "Leukemia",
      "title": "Leukemia distance matrix and classificiation used in [Thrun, 2018]",
      "topics": [
        "Leukemia"
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    },
    {
      "page": "Lsun3D",
      "title": "Lsun3D inspired by FCPS introduced in [Thrun, 2018]",
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    {
      "page": "MarkovClustering",
      "title": "Markov Clustering",
      "concept": [
        "Markov Clustering"
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      "topics": [
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    },
    {
      "page": "MeanShiftClustering",
      "title": "Mean Shift Clustering",
      "concept": [
        "Large Application Clusteringg"
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      "topics": [
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    },
    {
      "page": "MinimalEnergyClustering",
      "title": "Minimal Energy Clustering",
      "concept": [
        "Minimal Energy"
      ],
      "topics": [
        "MinimalEnergyClustering"
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    },
    {
      "page": "MinimaxLinkageClustering",
      "title": "Minimax Linkage Hierarchical Clustering",
      "concept": [
        "Minimax Linkage"
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      "topics": [
        "MinimaxLinkageClustering"
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    },
    {
      "page": "ModelBasedClustering",
      "title": "Model Based Clustering",
      "concept": [
        "Model based clustering",
        "Mixture Of Gaussians"
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      "topics": [
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    },
    {
      "page": "ModelBasedVarSelClustering",
      "title": "Model Based Clustering with Variable Selection",
      "concept": [
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        "Model-based clustering"
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      "topics": [
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    },
    {
      "page": "MoGclustering",
      "title": "Mixture of Gaussians Clustering using EM",
      "concept": [
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        "Expectation Maximization"
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      "topics": [
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      "title": "MST-kNN clustering algorithm [Inostroza-Ponta, 2008].",
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      "page": "NetworkClustering",
      "title": "Network Clustering",
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    },
    {
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      "title": "Neural gas algorithm for clustering",
      "concept": [
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      "topics": [
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    },
    {
      "page": "OPTICSclustering",
      "title": "OPTICS Clustering",
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    },
    {
      "page": "PAMClustering",
      "title": "Partitioning Around Medoids (PAM)",
      "concept": [
        "Partitioning Around Medoids"
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      "topics": [
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        "PAMclustering"
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    {
      "page": "parApplyClusterAnalysis",
      "title": "Repeated Application of a Clustering Method",
      "topics": [
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    },
    {
      "page": "parApplyDataBasedCA",
      "title": "Repeated Application of a Data-Based Clustering Method",
      "topics": [
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    {
      "page": "parApplyDistanceBasedCA",
      "title": "Repeated Application of a Distance-Based Clustering Method",
      "topics": [
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    {
      "page": "pdfClustering",
      "title": "Probability Density Distribution Clustering",
      "topics": [
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    },
    {
      "page": "PenalizedRegressionBasedClustering",
      "title": "Penalized Regression-Based Clustering of [Wu et al., 2016].",
      "concept": [
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        "Penalized Regression Based Clustering"
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      "topics": [
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      "page": "PFclustering",
      "title": "Power Fuzzy Clustering",
      "topics": [
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      "page": "ProjectionPursuitClustering",
      "title": "Cluster Identification using Projection Pursuit as described in [Hofmeyr/Pavlidis, 2019].",
      "topics": [
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      "page": "QTclustering",
      "title": "Stochastic QT Clustering",
      "topics": [
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        "QTclustering"
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    {
      "page": "RandomForestClustering",
      "title": "Random Forest Clustering",
      "concept": [
        "Random Forest Clustering"
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    },
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      "page": "RobustTrimmedClustering",
      "title": "Robust Trimmed Clustering",
      "concept": [
        "Robust Trimmed Clustering"
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      "topics": [
        "RobustTrimmedClustering"
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    },
    {
      "page": "SharedNearestNeighborClustering",
      "title": "SNN clustering",
      "concept": [
        "SharedNearest Neighbor Clustering"
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      "topics": [
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    },
    {
      "page": "SOMclustering",
      "title": "self-organizing maps based clustering implemented by [Wherens, Buydens, 2017].",
      "concept": [
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        "som clustering"
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      "topics": [
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    },
    {
      "page": "sotaClustering",
      "title": "SOTA Clustering",
      "concept": [
        "Self-organizing Tree Algorithm"
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      "topics": [
        "SOTAclustering",
        "sotaClustering"
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    },
    {
      "page": "SparseClustering",
      "title": "Sparse Clustering",
      "concept": [
        "Sparse Clustering"
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      "topics": [
        "SparseClustering"
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    },
    {
      "page": "SpectralClustering",
      "title": "Spectral Clustering",
      "concept": [
        "Spectral Clustering"
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      "topics": [
        "SpectralClustering"
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    },
    {
      "page": "Spectrum",
      "title": "Fast Adaptive Spectral Clustering [John et al, 2020]",
      "concept": [
        "Spectral Clustering"
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      "topics": [
        "Spectrum"
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    },
    {
      "page": "StatPDEdensity",
      "title": "Pareto Density Estimation",
      "concept": [
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        "Pareto Density Estimation"
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      "topics": [
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      "page": "SubspaceClustering",
      "title": "Algorithms for Subspace clustering",
      "concept": [
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      "concept": [
        "Tandem Clustering"
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      "title": "Target introduced in [Ultsch, 2005].",
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      "title": "WingNut introduced in [Ultsch, 2005]",
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