I am playing around with a dbscan example in order to see if it will work for me. In my case, I have clusters of a few points (3-5) close together with a fairly long distance in between clusters. I have tried to replicate the situation in the following code. I figured with a low epsilon and low min_samples,this should work, but instead it is telling me that it only sees 1 group (and 20 noise points?). Am I using this incorrectly, or is dbscan not good for this type of problem. I went with dbscan instead of kmeans because I dont know beforehand exactly how many clusters there will be (1-5).
from sklearn.datasets import make_blobs
from sklearn.cluster import DBSCAN
import numpy as np
import matplotlib.pyplot as plt
# Configuration options
num_samples_total = 20
cluster_centers = [(3,3), (7,7),(7,3),(3,7),(5,5)]
num_classes = len(cluster_centers)
#epsilon = 1.0
epsilon = 1e-5
#min_samples = 13
min_samples = 2
# Generate data
X, y = make_blobs(n_samples = num_samples_total, centers = cluster_centers, n_features = num_classes, center_box=(0, 1), cluster_std = 0.05)
np.save('./clusters.npy', X)
X = np.load('./clusters.npy')
# Compute DBSCAN
db = DBSCAN(eps=epsilon, min_samples=min_samples).fit(X)
labels = db.labels_
no_clusters = len(np.unique(labels) )
no_noise = np.sum(np.array(labels) == -1, axis=0)
print('Estimated no. of clusters: %d' % no_clusters)
print('Estimated no. of noise points: %d' % no_noise)
# Generate scatter plot for training data
colors = list(map(lambda x: '#3b4cc0' if x == 1 else '#b40426', labels)) #only set for 2 colors
plt.scatter(X[:,0], X[:,1], c=colors, marker="o", picker=True)
plt.title('Two clusters with data')
plt.xlabel('Axis X[0]')
plt.ylabel('Axis X[1]')
plt.show()
ended up going with kmeans and doing a modified elbow method:
print(__doc__)
# Author: Phil Roth <mr.phil.roth@gmail.com>
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
# Configuration options
num_samples_total = 20
cluster_centers = [(3,3), (7,7),(7,3),(3,7),(5,5)]
num_classes = len(cluster_centers)
#epsilon = 1.0
epsilon = 1e-5
#min_samples = 13
min_samples = 2
# Generate data
X, y = make_blobs(n_samples = num_samples_total, centers = cluster_centers, n_features = num_classes, center_box=(0, 1), cluster_std = 0.05)
random_state = 170
#y_pred = KMeans(n_clusters=5, random_state=random_state).fit_predict(X)
#plt.scatter(X[:, 0], X[:, 1], c=y_pred)
#kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
#maybe I dont have to look for an elbow, just go until the value drops below 1.
#also if I do go too far, it just means that the same shape will be shown twice.
clusterIdx = 0
inertia = 100
while inertia > 1:
clusterIdx = clusterIdx + 1
kmeans = KMeans(n_clusters=clusterIdx, random_state=0).fit(X)
inertia = kmeans.inertia_
print(inertia)
plt.scatter(X[:, 0], X[:, 1], c=kmeans.labels_)
print(clusterIdx)
plt.show()