__K-Means Clustering : Introduction__

__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.**

__Criterion Function : Clustering__

__Criterion Function : Clustering__

**To measure the quality of clustering ability of any partitioned data set, criterion function is used.****Consider a set , B = { x**_{1},x_{2},x_{3}…x_{n}} containing “n” samples, that is partitioned exactly into “t” disjoint subsets i.e. B_{1}, B_{2},…..,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.**

**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.**

**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.**

**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**