Implementing k-means algorithm for cluster analysis on multivariate and multidimensional data

I have a set of 1000 models. Each model is a (72 x 4) matrix, where 72 are the values associated to each of the 4 variables. The goal is to perform cluster analysis on these models, i.e. to group them based on a similar distribution of all the 4 variables. It turns out that this is a multidimensional (72) and multivariate (4) problem.

I am trying to implement it in Python by using the k-means algorithm implemented in tensorflow module. First, I have transformed the input dataset to a ndarray (1000 x 288), where the rows are the models and the columns the 288 = 72 x 4 values (this should be fine, since I need to perform cluster analysis based on all the variables). In a sense, I have addressed the multivariate issue by converting the problem to a "univariate" problem.

As a second step, I have standardized the array, so to map the variable values to a common scale.

models = np.array(models)
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()

models_scaled = scaler.fit_transform(models.transpose())
models_scaled = models_scaled.transpose()

import tensorflow as tf

#def input_fn():
#return tf.constant(models_par, dtype=tf.float32)

def input_fn():
return tf.compat.v1.train.limit_epochs(
tf.convert_to_tensor(models_scaled, dtype=tf.float32), num_epochs=1)

num_clusters = 5
kmeans = tf.compat.v1.estimator.experimental.KMeans(num_clusters=num_clusters, relative_tolerance=0.001)

# train
num_iterations = 20
previous_centers = None
for _ in range(num_iterations):
kmeans.train(input_fn)
cluster_centers = kmeans.cluster_centers()
if previous_centers is not None:
print('delta: ', (cluster_centers - previous_centers))
previous_centers = cluster_centers
print('score: ' , (kmeans.score(input_fn)))
print('cluster centers: ' , (cluster_centers))

# map the input points to their clusters
cluster_indices = list(kmeans.predict_cluster_index(input_fn))
for i, point in enumerate(models_scaled):
cluster_index = cluster_indices[i]
center = cluster_centers[cluster_index]
print('point: ' , (point) , 'is in cluster ' , (cluster_index) , 'centered at ' , (center))

Assuming this is all correct up to this point, I have troubles in visualizing the output returned by tensorflow. I am trying to get a parallel coordinate plot:

X_clustered = pd.DataFrame()
X_clustered['feature_vector'] = models_scaled.tolist()
X_clustered['cluster'] = cluster_indices

The code above is obviously wrong. Although I have solved (?) the multivariate aspect of the problem, the issue is it's still multidimensional. You may argue I could reduce dimensionality by implementing PCA, for instance, but for the parallel coordinate plot I think the original dataset is required.