Orsdxy

I am trying to determine which method mathematica chooses when using FindClusters. The documentation says that it chooses the best one for the data. I have tried to use AbsoluteOptions, which says it returns the options for a command, but it does not seem to be working.

GaussianRandomData[n_Integer, p_, sigma_] := 

  Table[p + 

    {Re[#], Im[#]}&[RandomReal[NormalDistribution[0, sigma]] E^(I RandomReal[{0, 2 π}])], {n}];

datapairs = BlockRandom[SeedRandom[2134];

Join[

  GaussianRandomData[100, {2, 1}, .3], 

  GaussianRandomData[100, {1, 1.8}, .2], 

  GaussianRandomData[100, {1, 1.1}, .4], 

  GaussianRandomData[100, {1.75, 1.75}, 0.1]]];



AbsoluteOptions[FindClusters[datapairs, Method -> Automatic], Method]

Any help would be appreciated.

Using Trace with the option TraceInternal -> True gives:

DeleteDuplicates[Flatten@Trace[FindClusters[datapairs, Method -> Automatic], 

   HoldPattern[Rule["Method", _]], TraceInternal -> True]]

{"Method"->"GaussianMixture"}

If you specify the number of clusters:

DeleteDuplicates[Flatten@Trace[FindClusters[datapairs, 3, Method -> Automatic], 

   HoldPattern[Rule["Method", _]], TraceInternal -> True]]

{"Method"->"KMeans"}

With PerformanceGoal -> "Quality"

DeleteDuplicates[Flatten@Trace[FindClusters[datapairs, 3, Method -> Automatic, 

    PerformanceGoal -> "Quality"], HoldPattern[Rule["Method", _]], 

   TraceInternal -> True]]

{"Method"->"KMedoids"}

l = {RGBColor[1., 0.5544801460824762, 0.12056345655596812`], RGBColor[

   1., 0.2818404077149421, 0.1073945311994069], RGBColor[

   1., 0.12423838985259317`, 0.19023691956664956`], RGBColor[

   0.8, 0.4542154246540884, 0.31688034954543], RGBColor[

   0.8, 0.5483770742736782, 0.16977938137471082`], RGBColor[

   0.8, 0.03163746197875539, 0.5781619271042624], RGBColor[

   0.8, 0.1612089376881538, 0.15737556414394493`], RGBColor[

   0.5, 0.8592283961197744, 0.04768022523989446], RGBColor[

   0.1544029090531034, 0.5400111921283921, 0.1332688011328087], 

   RGBColor[0.5550268260924609, 0.6650311925481958, 0.24096295360192643`], 

   RGBColor[0.8424867588418756, 0.9610747917029776, 0.38159472421539053`], 

   RGBColor[0.5, 0.6654316628707297, 0.9850955091132039], RGBColor[

   0.1726013976586489, 0.7948159289195966, 0.9375970360424373], 

   RGBColor[0.07338116039584297, 0.6615692536088942, 0.9035903703739081], 

   RGBColor[0.0396922307314016, 0.06815211658088716, 0.9401879243429714], 

   RGBColor[0.26561262398696184`, 0.1750699399994622, 0.47868645290098866`]};



DeleteDuplicates[Flatten@Trace[FindClusters[l], HoldPattern[Rule["Method", _]], 

   TraceInternal -> True]]

{Method -> DBSCAN}

The function MachineLearning`file40Decisions`PackagePrivate`automaticClusterNumberMethods seems to determine the method to be used based on input type, data dimensions and the setting for the option PerformanceGoal:

automaticClusterNumberMethods[type_, performanceGoal_, dims_]:= If[

    MachineLearning`file40Decisions`PackagePrivate`vectorSpaceQ[type],

    Switch[

            performanceGoal, Automatic | "Memory",

                If[Greater[Last @ dims, 7],

                    {"DBSCAN", "NeighborhoodContraction", "Agglomerate"},

                    {"DBSCAN", "NeighborhoodContraction", "GaussianMixture", 

      "Agglomerate"}

                ],

            "Speed",

                {"DBSCAN", "GaussianMixture", "NeighborhoodContraction"},

            "Quality",

                {

                    "Agglomerate", "DBSCAN", "JarvisPatrick", "MeanShift", 

     "Spectral", "SpanningTree",

                    "NeighborhoodContraction", "GaussianMixture"

                },

            "TrainingSpeed",

                {"DBSCAN", "NeighborhoodContraction"}

        ],

    {"DBSCAN", "JarvisPatrick"}

   ];

If the number of clusters is given the function MachineLearning`file40Decisions`PackagePrivate`givenClusterNumberMethods is called to determine the method to be used:

givenClusterNumberMethods[type_, performanceGoal_] := If[

    vectorSpaceQ[type],

    Switch[

        performanceGoal, Automatic | "Memory" | "Speed",

            {"KMeans", "Agglomerate"},

        "Quality",

            {"KMeans", "Agglomerate", "Spectral", "KMedoids"},

        "TrainingSpeed",

            {"KMeans"}

    ],

    If[MatchQ[type, {"Location"}],

        {"KMedoids"},

        {"KMedoids", "Agglomerate"}

    ]

];