Em algorithm(Expectation Maximization) and Hierarchical Cluster

Em algorithm(Expectation Maximization) and Hierarchical Cluster

Em algorithm(Expectation Maximization)-

The EM algorithm is the extension of K means algorithm. The EM algorithm is assign each object to a cluster according their weight representation. The probability are mention here for clustering definition they are based on weighted and measures of objects.

Hierarchical Method Cluster-

It is working for grouping data objects into cluster. It is divided into two types-

  1. Aaglomerative Hierarchical Clustering 
  2. Divisible Hierachical Clustering

1. Aaglomerative Hierarchical Clustering-

It is fallow bottom-up strategy. In that merging a small atomic cluster into larger cluster. That process is repeated until the termination condition holds.

2. Divisible Hierachical Clustering-

It follows the top-down strategy and it is Reverse process of Aaglomerative hierarchical clustering. That is starting with all objects with one cluster and subdivided cluster into smaller unit until termination condition not satisfied well in Manner.

Amol Shinde

Amol Shinde

Work as Assistant Professor and Web Developer.

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