K-Means Clustering : Introduction

• K-Means clustering is known to be one of the simplest unsupervised learning algorithms that is capable of solving well known clustering problems.
• K-Means clustering algorithm can be executed in order to solve a problem using four simple steps:
  • Make the partition of objects into K non empty steps i.e. K=1,2,3,.. .
  • Consider arbitrary seed points from sample data.
  • Calculate mean distance of sample data from seed points in order to generate clusters.
  • Repeat the above steps until values of two clusters becomes same. Below is an solved example.

Example : K-means Clustering

Criterion Function : Clustering

• To measure the quality of clustering ability of any partitioned data set, criterion function is used.
• Consider a set , B = { x₁,x₂,x₃…x_n} containing “n” samples, that is partitioned exactly into “t” disjoint subsets i.e. B₁, B₂,…..,B_t.
• The main highlight of these subsets is, every individual subset represents a cluster.
• Sample inside the cluster will be similar to each other and dissimilar to samples in other clusters.
• To make this possible, criterion functions are used according the occurred situations.

Criterion Function For Clustering

1. Internal Criterion Function

• This class of clustering is an intra-cluster view.
• Internal criterion function optimizes a function and measures the quality of clustering ability various clusters which are different from each other.

2. External Criterion Function

• This class of clustering criterion is an inter-class view.
• External Criterion Function optimizes a function and measures the quality of clustering ability of various clusters which are different from each other.

3. Hybrid Criterion Function

• This function is used as it has the ability to simultaneously optimize multiple individual Criterion Functions unlike as Internal Criterion Function and External Criterion Function