UNIT II
,
,CLUSTRING PARADIGMS
(i) Hierarchical Clustering in Data Mining
• A Hierarchical clustering method works via grouping data into a tree
clusters. Hierarchical clustering begins by treating every data points
separate cluster. Then, it repeatedly executes the subsequent steps:
• Identify the 2 clusters which can be closest together, and
• Merge the 2 maximum comparable clusters. We need to continue th
steps until all the clusters are merged together.
In Hierarchical Clustering, the aim is to produce a hierarchical series o
nested clusters.
, • The basic method to generate hierarchical clustering are:
• 1. Agglomerative:
Initially consider every data point as an individual Cluster and at ev
step, merge the nearest pairs of the cluster. (It is a bottom-up
method). At first everydata set set is considered as individual entity
cluster. At every iteration, the clusters merge with different clusters
until one cluster is formed.
,
,CLUSTRING PARADIGMS
(i) Hierarchical Clustering in Data Mining
• A Hierarchical clustering method works via grouping data into a tree
clusters. Hierarchical clustering begins by treating every data points
separate cluster. Then, it repeatedly executes the subsequent steps:
• Identify the 2 clusters which can be closest together, and
• Merge the 2 maximum comparable clusters. We need to continue th
steps until all the clusters are merged together.
In Hierarchical Clustering, the aim is to produce a hierarchical series o
nested clusters.
, • The basic method to generate hierarchical clustering are:
• 1. Agglomerative:
Initially consider every data point as an individual Cluster and at ev
step, merge the nearest pairs of the cluster. (It is a bottom-up
method). At first everydata set set is considered as individual entity
cluster. At every iteration, the clusters merge with different clusters
until one cluster is formed.
