2. A Hierarchical clustering method
works via grouping data into a tree of
clusters. Hierarchical clustering begins
by treating every data point as a
separate cluster.
All the examples are of fruit of
different kinds.
3. In contrast to non-hierarchical cluster analysis, hierarchical
cluster analysis forms clusters iteratively, by successively
joining or splitting groups.
There are two kinds:
Divisive - which starts with the entire data set in one large
group and then successively splits it into smaller groups
until each observation is its own group.
Agglomerative - in which each observation starts in its own
group, and groups are successively paired until at the end
every observation is in the same large group.
Divisive methods are computationally intensive and have
had limited applications in the social sciences.
Agglomerative methods have been implemented in many
standard software packages.
4. Initially consider every data point as an individual Cluster and
at every step, merge the nearest pairs of the cluster. (It is a
bottom-up method). At first, every dataset is considered as an
individual entity or cluster. At every iteration, the clusters
merge with different clusters until one cluster is formed.
The algorithm for Agglomerative Hierarchical Clustering is:
1. Calculate the similarity of one cluster with all the other
clusters (calculate proximity matrix)
2. Consider every data point as an individual cluster
3. Merge the clusters which are highly similar or close to each
other.
4. Recalculate the proximity matrix for each cluster
5. Repeat Steps 3 and 4 until only a single cluster remains.
6. Let’s see the graphical representation of this algorithm using
a dendrogram.
5. We can say that the Divisive
Hierarchical clustering is
precisely the opposite of the
Agglomerative Hierarchical
clustering. In Divisive
Hierarchical clustering, we
take into account all of the
data points as a single cluster
and in every iteration, we
separate the data points from
the clusters which aren’t
comparable. In the end, we
are left with N clusters.
6. It is to understand and implement.
We don’t have to pre-specify any particular number of clusters.
Can obtain any desired number of clusters by cutting the Dendrogram
at the proper level.
They may correspond to meaningful classification.
Easy to decide the number of clusters by merely looking at the
Dendrogram.
7. Hierarchical Clustering does not work well on vast amounts of data.
All the approaches to calculate the similarity between clusters have their own
disadvantages.
In hierarchical Clustering, once a decision is made to combine two clusters, it cannot be
undone.
Different measures have problems with one or more of the following.
Sensitivity to noise and outliers.
Faces Difficulty when handling with different sizes of clusters.
It is breaking large clusters.
In this technique, the order of the data has an impact on the final results.
8. Here I have taken fish buying behaviour of consumers. There are 5 Question with
the answer with 6 categories. Like 1 – Strongly Disagree, 2 – little Disagree, 3-
disagree, 4- Little Agree, 5- Agree, 6- Strongly Agree. Here I have taken the data
from 25 respondents.
Q1: I love to buy Fish in market every day.
Q2: I buy fish in discounted price.
Q3: I like to bargain while buying.
Q4: I compare the price of the fishes in various shop.
Q5: I enjoy Eating Fish products.
