Unsupervised

KMeans

Advantages

  • Good when you have an idea of an ideal number of clusters
  • Can scale well with lots of samples, scale medium with number of clusters

Disadvantages

  • Doesn’t handle missing values very well
  • Can’t find clusters that aren’t circular or spherical

Choosing the value of K

For choosing the value of k cluster we can use the elbow method:

from sklearn.clusters import Kmeans
from sklearn.metrics import silhouette_score

X = pd.DataFrame(...)

possible_k_values = range(2, len(X)+1, 5)

scores = []
for k in possible_k_values:
    model = Kmeans(n_clusters=k).fit(X)
    prediction = model.predict(X)
    score = silhouette_score(X, predictions)
    scores.append((k, score))

Then find the best numbers of clusters by choosing a k that has a lower score of errors but can still be good enough for your problem.

Hierarchical Clustering

Advantages

  • Resulting hierarchical representation can be very informative
  • Provides an additional ability to visualize
  • Especially potent when the dataset contains real hierarchical relationship (e.g. Evolutionary biology)

Disadvantages

  • Sensitive to noise and outliers
  • Computationally intensive O(N^2)

Implementation on Sklearn

from sklearn import cluster

X = pd.DataFrame(...)

cls = cluster.AgglomerativeClustering(n_clusters=3, linkage='ward')
labels = cls.predict(X)

Get a dendrogram from a hierarchical clustering

from scipy.cluster.hierarchy import dendogram, ward
import matplotlib.pyplot as plt

X = pd.DataFrame(...)
linkage_matrix = ward(X)

dendogram(linkage_matrix)
plt.show()

DBSCAN

Advantages:

  • We don’t need to specify the number of clusters
  • Flexibility in shapes and sizes of clusters
  • Able to deal with noise and outliers

Disadvantages

  • Border points that are reachable from two clusters is assigned to the cluster that finds it first
  • Faces difficulty finding clusters of varying densities

Tips:

  • Small min samples and small episilon results in many small clusters
  • Small min samples and large episilon results in most points being on the same cluster
  • Large min samples results in most of points being classified as noise, except on desen regions when episilon is high
  • Do not use silhouetter coefficient to test this model! Recomendado

Gaussian Mixture Model

Advantages

  • Soft-clustering (you can see percentages of cluster participation on each sample)
  • Cluster shape flexibility

Disadvantages

  • Sensitive to initialization values
  • Possible to converge to a local optimum
  • Slow convergence rate

General References


Cheers!
Leticia

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