diff --git a/release-packaging/HelpSource/Classes/FluidKMeans.schelp b/release-packaging/HelpSource/Classes/FluidKMeans.schelp
index 8c49e9a..58d895c 100644
--- a/release-packaging/HelpSource/Classes/FluidKMeans.schelp
+++ b/release-packaging/HelpSource/Classes/FluidKMeans.schelp
@@ -25,7 +25,7 @@ INSTANCEMETHODS::
 PRIVATE::k
 
 METHOD:: fit
-Identify code::k:: clusters in a link::Classes/FluidDataSet::
+Identify code::numClusters:: clusters in a link::Classes/FluidDataSet::. It will optimise until no improvement is possible, or up to code::maxIter::, whichever comes first. Subsequent calls will continue training from the stopping point with the same conditions.
 ARGUMENT:: dataSet
 A link::Classes/FluidDataSet:: of data points.
 ARGUMENT:: action
@@ -56,55 +56,51 @@ a link::Classes/Buffer:: containing a data point.
 ARGUMENT:: action
 A function to run when the server responds, taking the ID of the cluster as its argument.
 
-METHOD:: predict
-Report cluster assignments for previously unseen data.
-ARGUMENT:: dataSet
-A link::Classes/FluidDataSet:: of data points.
-ARGUMENT:: labelSet
-A link::Classes/FluidLabelSet:: to contain assignments.
+METHOD:: transform
+Given a trained object, return for each item of a provided DataSet its distance to each cluster as an array, often reffered to as the cluster-distance space.
+ARGUMENT:: srcDataSet
+A link::Classes/FluidDataSet:: of data points to transform.
+ARGUMENT:: dstDataSet
+A link::Classes/FluidDataSet:: to contain the new cluster-distance space.
 ARGUMENT:: action
-A function to run when complete, taking an array of the counts for each category as its argument.
+A function to run when complete.
 
 METHOD:: fitTransform
-Run link::Classes/FluidKMeans#*fit:: and link::Classes/FluidKMeans#*predict:: in a single pass: i.e. train the model on the incoming link::Classes/FluidDataSet:: and then return the learned clustering to the passed link::Classes/FluidLabelSet::
+Run link::Classes/FluidKMeans#*fit:: and link::Classes/FluidKMeans#*transform:: in a single pass: i.e. train the model on the incoming link::Classes/FluidDataSet:: and then return its cluster-distance space in the destination link::Classes/FluidDataSet::
 ARGUMENT:: srcDataSet
 a link::Classes/FluidDataSet:: containing the data to fit and predict.
 ARGUMENT:: dstDataSet
-a link::Classes/FluidLabelSet:: to retrieve the predicted clusters.
+A link::Classes/FluidDataSet:: to contain the new cluster-distance space.
 ARGUMENT:: action
-A function to run when the server responds
+A function to run when complete.
 
 METHOD:: transformPoint
-Given a trained object, return the cluster ID for a data point in a link::Classes/Buffer::
+Given a trained object, return the distance of the provided point to each cluster. Both points are handled as link::Classes/Buffer::
 ARGUMENT:: sourceBuffer
-a link::Classes/Buffer:: containing a data point.
+a link::Classes/Buffer:: containing a data point to query.
 ARGUMENT:: targetBuffer
-a link::Classes/Buffer:: containing a data point.
-ARGUMENT:: action
-A function to run when the server responds, taking the ID of the cluster as its argument.
-
-METHOD:: transform
-Report cluster assignments for previously unseen data.
-ARGUMENT:: srcDataSet
-A link::Classes/FluidDataSet:: of data points.
-ARGUMENT:: dstDataSet
-A link::Classes/FluidLabelSet:: to contain assignments.
+a link::Classes/Buffer:: containing a the distance of the source point to each cluster.
 ARGUMENT:: action
-A function to run when complete, taking an array of the counts for each category as its argument.
+A function to run when complete.
 
 METHOD:: getMeans
-Report cluster assignments for previously unseen data.
+Given a trained object, retrieve the means (centroids) of each cluster as a link::Classes/FluidDataSet::
 ARGUMENT:: dataSet
-A link::Classes/FluidDataSet:: of data points.
+A link::Classes/FluidDataSet:: of clusers with a mean per column.
 ARGUMENT:: action
-A function to run when complete, taking an array of the counts for each category as its argument.
+A function to run when complete.
 
 METHOD:: setMeans
-Report cluster assignments for previously unseen data.
+Overwrites the means (centroids) of each cluster, and declare the object trained.
 ARGUMENT:: dataSet
-A link::Classes/FluidDataSet:: of data points.
+A link::Classes/FluidDataSet:: of clusers with a mean per column.
+ARGUMENT:: action
+A function to run when complete.
+
+METHOD:: clear
+Reset the object status to not fitted and untrained.
 ARGUMENT:: action
-A function to run when complete, taking an array of the counts for each category as its argument.
+A function to run when complete.
 
 EXAMPLES::
 code::
@@ -192,30 +188,40 @@ subsection:: Accessing the means
 We can get and set the means for each cluster, their centroid.
 
 code::
+// with the dataset and kmeans generated and trained in the code above
 ~centroids = FluidDataSet(s);
 ~kmeans.getMeans(~centroids, {~centroids.print});
 
-
-
+// We can also set them to arbitrary values to seed the process
 ~centroids.load(Dictionary.newFrom([\cols, 2, \data, Dictionary.newFrom([\0, [0.5,0.5], \1, [-0.5,0.5], \2, [0.5,-0.5], \3, [-0.5,-0.5]])]));
 ~centroids.print
 ~kmeans.setMeans(~centroids, {~kmeans.predict(~dataSet,~clusters,{~clusters.dump{|x|var count = 0.dup(4); x["data"].keysValuesDo{|k,v|count[v[0].asInteger] = count[v[0].asInteger] + 1;};count.postln}})});
 
-~kmeans.clear
-~kmeans.predict(~dataSet,~clusters)
-
+// We can further fit from the seeded means
+~kmeans.fit(~dataSet)
+// then retreive the improved means
+~kmeans.getMeans(~centroids, {~centroids.print});
+//subtle in this case but still.. each quadrant is where we seeded it.
 ::
 
 subsection:: Cluster-distance Space
 
-You can get the euclidian distance of a given point to each cluster.
+We can get the euclidian distance of a given point to each cluster. This is often referred to as the cluster-distance space as it creates new dimensions for each given point, one distance per cluster.
 
 code::
+// with the dataset and kmeans generated and trained in the code above
 b = Buffer.sendCollection(s,[0.5,0.5])
 c = Buffer(s)
 
+// get the distance of our given point (b) to each cluster, thus giving us 4 dimensions in our cluster-distance space
 ~kmeans.transformPoint(b,c,{|x|x.query;x.getn(0,x.numFrames,{|y|y.postln})})
 
+// we can also transform a full dataset
+~srcDS = FluidDataSet(s)
+~cdspace = FluidDataSet(s)
+// make a new dataset with 4 points
+~srcDS.load(Dictionary.newFrom([\cols, 2, \data, Dictionary.newFrom([\pp, [0.5,0.5], \np, [-0.5,0.5], \pn, [0.5,-0.5], \nn, [-0.5,-0.5]])]));
+~kmeans.transform(~srcDS, ~cdspace, {~cdspace.print})
 ::
 
 subsection:: Queries in a Synth