I'm using TSNE to visualize my clusters but the output seems a bit strange. There are supposed to be 3 clusters but instead, there are 4 lines. Is there something wrong with how I'm visualizing them or is it the kmeans method itself?
import pandas as pd
import numpy as np
import ast
from sklearn import metrics
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from sklearn.decomposition import TruncatedSVD
colNames = ['unixTime', 'sampleAmount','Time','samplingRate', 'Data']
data = pd.read_csv("project_fan.csv", sep = ';', error_bad_lines = False, names = colNames)
# changing data into list
data['Data'] = data.Data.transform(ast.literal_eval)
# Selecting the average value from the list and replacing the list with it
data['Data'] = data.Data.apply(np.mean)
kmeanModel = KMeans(n_clusters = 3)
kmeanModel.fit(data)
y = kmeanModel.labels_
X_train, X_test, y_train, y_test = train_test_split(data, y, test_size = 0.2, random_state = 1)
k = 3
tfs_reduced = TruncatedSVD(n_components=k, random_state=0).fit_transform(data)
tfs_embedded = TSNE(n_components=2, perplexity=40, verbose=2).fit_transform(tfs_reduced)
fig = plt.figure(figsize = (10, 10))
ax = plt.axes()
plt.scatter(tfs_embedded[:, 0], tfs_embedded[:, 1], marker = "x", c = km.labels_)
plt.show()
Sample Dataset:
unixTime sampleAmount Time samplingRate Data
0 1.556891e+09 16384 340 48188.235294 1620.242170
1 1.556891e+09 16384 341 48046.920821 1620.237716
2 1.556891e+09 16384 340 48188.235294 1620.236340
3 1.556891e+09 16384 340 48188.235294 1620.229289
4 1.556891e+09 16384 340 48188.235294 1620.227541
Output:
