Adjust some parameter help text and moved the section on randomness h…

…igher up in the docs

Signed-off-by: Michael Jackson <[email protected]>

src/Plugins/SimplnxCore/docs/DBSCANFilter.md

@@ -66,6 +66,10 @@ A clustering algorithm can be considered a kind of segmentation; this implementa
 | Face Ensemble | Face Ensemble |
 | Cell Ensemble | Cell Ensemble|
 
+## Note on Randomness
+
+It is not recommended to use iterative for the _Initalization Type_, as it was just included for backwards compatibility. The inclusion of randomness in this algorithm is solely to attempt to reduce bias from starting cluster. Iterative produced identical results in our test cases, but the random initialization is truest to the well known DBSCAN algorithm.
+
 % Auto generated parameter table will be inserted here
 
 ## Notes on Hyperparameter Tuning
@@ -97,10 +101,6 @@ This dataset is not the ideal case for this algorithm, but it is what we were ab
 
 _Note from Developers_: We are aware of a paper that outlines an algorithm that can reasonably predict the hyperparameter values, but at the current time implementation is left up to potential contributors [2].
 
-## Note on Randomness
-
-It is not recommended to use iterative for the _Initalization Type_, as it was just included for backwards compatibility. The inclusion of randomness in this algorithm is solely to attempt to reduce bias from starting cluster. Iterative produced identical results in our test cases, but the random initialization is truest to the well known DBSCAN algorithm.
-
 ## References
 
 [1] A density-based algorithm for discovering clusters in large spatial databases with noise, M. Ester, H.P. Kriegel, J. Sander, and X. Xu, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 226-231, 1996.

src/Plugins/SimplnxCore/src/SimplnxCore/Filters/DBSCANFilter.cpp

@@ -57,7 +57,7 @@ std::string DBSCANFilter::humanName() const
 //------------------------------------------------------------------------------
 std::vector<std::string> DBSCANFilter::defaultTags() const
 {
-  return {className(), "DREAM3D Review", "Clustering"};
+  return {className(), "DBSCAN", "Clustering", "Segmentation", "Statistics"};
 }
 
 //------------------------------------------------------------------------------
@@ -89,12 +89,13 @@ Parameters DBSCANFilter::parameters() const
                                                           ArraySelectionParameter::AllowedTypes{DataType::boolean, DataType::uint8}));
 
   params.insertSeparator(Parameters::Separator{"Input Data Objects"});
-  params.insert(std::make_unique<ArraySelectionParameter>(k_SelectedArrayPath_Key, "Attribute Array to Cluster", "The array to cluster from", DataPath{}, nx::core::GetAllNumericTypes()));
+  params.insert(std::make_unique<ArraySelectionParameter>(k_SelectedArrayPath_Key, "Attribute Array to Cluster", "The data array to cluster", DataPath{}, nx::core::GetAllNumericTypes()));
 
   params.insertSeparator(Parameters::Separator{"Output Data Object(s)"});
-  params.insert(std::make_unique<DataObjectNameParameter>(k_FeatureIdsArrayName_Key, "Cluster Ids Array Name", "name of the ids array to be created in Attribute Array to Cluster's parent group",
+  params.insert(std::make_unique<DataObjectNameParameter>(k_FeatureIdsArrayName_Key, "Cluster Ids Array Name", "Name of the ids array to be created in Attribute Array to Cluster's parent group",
                                                           "Cluster Ids"));
-  params.insert(std::make_unique<DataGroupCreationParameter>(k_FeatureAMPath_Key, "Cluster Attribute Matrix", "name and path of Attribute Matrix to hold Cluster Data", DataPath{}));
+  params.insert(
+      std::make_unique<DataGroupCreationParameter>(k_FeatureAMPath_Key, "Cluster Attribute Matrix", "The complete path to the attribute matrix in which to store to hold Cluster Data", DataPath{}));
 
   // Associate the Linkable Parameter(s) to the children parameters that they control
   params.linkParameters(k_InitTypeIndex_Key, k_SeedArrayName_Key, static_cast<ChoicesParameter::ValueType>(to_underlying(AlgType::Random)));