diff --git a/analysis-master/tra_analysis/Analysis.py b/analysis-master/tra_analysis/Analysis.py
index 6a8a63b3..df81951c 100644
--- a/analysis-master/tra_analysis/Analysis.py
+++ b/analysis-master/tra_analysis/Analysis.py
@@ -599,7 +599,7 @@ def npmin(data):
 def npmax(data):
 
 	return np.amax(data)
-
+""" need to decide what to do with this function
 def kmeans(data, n_clusters=8, init="k-means++", n_init=10, max_iter=300, tol=0.0001, precompute_distances="auto", verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm="auto"):
 
 	kernel = sklearn.cluster.KMeans(n_clusters = n_clusters, init = init, n_init = n_init, max_iter = max_iter, tol = tol, precompute_distances = precompute_distances, verbose = verbose, random_state = random_state, copy_x = copy_x, n_jobs = n_jobs, algorithm = algorithm)
@@ -608,7 +608,7 @@ def kmeans(data, n_clusters=8, init="k-means++", n_init=10, max_iter=300, tol=0.
 	centers = kernel.cluster_centers_
 
 	return centers, predictions
-
+"""
 def pca(data, n_components = None, copy = True, whiten = False, svd_solver = "auto", tol = 0.0, iterated_power = "auto", random_state = None):
 
 	kernel = sklearn.decomposition.PCA(n_components = n_components, copy = copy, whiten = whiten, svd_solver = svd_solver, tol = tol, iterated_power = iterated_power, random_state = random_state)
diff --git a/analysis-master/tra_analysis/Clustering.py b/analysis-master/tra_analysis/Clustering.py
new file mode 100644
index 00000000..35988715
--- /dev/null
+++ b/analysis-master/tra_analysis/Clustering.py
@@ -0,0 +1,30 @@
+# Titan Robotics Team 2022: Clustering submodule
+# Written by Arthur Lu
+# Notes:
+#    this should be imported as a python module using 'from tra_analysis import Clustering'
+# setup:
+
+__version__ = "1.0.0"
+
+# changelog should be viewed using print(analysis.__changelog__)
+__changelog__ = """changelog:
+    1.0.0:
+        - created this submodule
+        - copied kmeans clustering from Analysis
+"""
+
+__author__ = (
+	"Arthur Lu <learthurgo@gmail.com>",
+)
+
+__all__ = [
+]
+
+def kmeans(data, n_clusters=8, init="k-means++", n_init=10, max_iter=300, tol=0.0001, precompute_distances="auto", verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm="auto"):
+
+	kernel = sklearn.cluster.KMeans(n_clusters = n_clusters, init = init, n_init = n_init, max_iter = max_iter, tol = tol, precompute_distances = precompute_distances, verbose = verbose, random_state = random_state, copy_x = copy_x, n_jobs = n_jobs, algorithm = algorithm)
+	kernel.fit(data)
+	predictions = kernel.predict(data)
+	centers = kernel.cluster_centers_
+
+	return centers, predictions
\ No newline at end of file