Unsupervised learning and clustering algorithms

Last updated on:2 years ago

Unlabelled data is also a key type of input data of machine learning. Usually, we will cluster these data to discover their structures.

In unsupervised learning, you are given an unlabelled dataset and are asked to find “structure” in the data.

Clustering techniques

Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters).

Hierarchical clustering algorithms, Mixture-resolving and mode-seeking algorithms, nearest neighbour clustering, Fuzzy clustering, etc.

Partitional algorithms

Squared error clustering method

$$e^2(x, l) = \sum^K_{j=1}\sum^{n_j}{i=1}||x_i^{(j)}-c_j||^2$$

K-means algorithm

Cluster centroid

In mathematics and physics, the centroid or geometric centre of a plane figure is the arithmetic mean position of all the points in the figure.


  • Cluster assignment step - minimize J, wrt $c^{(1)}, c^{(2)}, c^{(k)}$
  • Move cluster centroid step - minimize J , with reference to $\mu_1, \mu_2, \mu_k$


  • K (number of clusters)
  • Unlabelled Training set

Randomly initialize K cluster centroid

  • Should have $K<m$

  • Randomly pick K training examples

Set $\mu_1, … , \mu_K$ equal to these K examples

Try multiple random initializations. Pick clustering theta gave lowest cost J

Optimization objective

$$J(c^{(1)}, …, c^{(m)}, \mu_1, …, \mu_K) = $$

$$\frac{1}{m}\sum^m_{i=1}||x^{(i)} - \mu_{c^{(i)}}||^2$$

Minimize $J(c^{(1)}, …, c^{(m)}, \mu_1, …, \mu_K) $.

Choosing the number of clusters

Elbow method


Graph-theoretic clustering

Using the minimal spanning tree to form clusters


Image segmentation, object and character recognition, information retrieval, data mining.


[1] Andrew NG, Machine learning

[2] Centroid

[3] Jain, A.K., Murty, M.N. and Flynn, P.J., 1999. Data clustering: a review. ACM computing surveys (CSUR), 31(3), pp.264-323.