This document discusses two-step cluster analysis. It explains that two-step cluster analysis is a hybrid approach that first separates groups using a distance measure, and then chooses the optimal subgroup model using a probabilistic approach. It can handle both categorical and continuous variables by assuming variable independence within clusters. The algorithm can also automatically select the optimal number of clusters and is scalable to large datasets. The document provides an example of two-step cluster analysis using designation and education as categorical variables, and age and experience as continuous variables to cluster 30 individuals into three groups based on these characteristics.

1. TWO STEP
CLUSTER
ANALYSIS
SUBMITTED TO:
PROF. SOMEN SAHU
DEPT. OF FES
SUBMITTED BY –
AGNIVA PRADHAN
M.F.Sc 2ND SEMESTER
DEPT. OF FNT
M/F/2021/03

2. The Two-Step cluster analysis is a hybrid approach which
first uses a distance measure to separate groups and then
a probabilistic approach (similar to latent class analysis)
to choose the optimal subgroup model
Two step cluster analysis uses a likelihood distance
measured which assumes that variables in a cluster model
are independent.

3. Handling of categorical and continuous variables. By
assuming variables to be independent, a joint
multinomial-normal distribution can be placed on
categorical and continuous variables.
Automatic selection of number of clusters. By comparing
the values of a model-choice criterion across different
clustering solutions, the procedure can automatically
determine the optimal number of clusters.
Scalability. By constructing a cluster features (CF) tree
that summarizes the records, the Two-step algorithm
allows you to analyze large data files.

4. Retail and consumer product companies regularly apply
clustering techniques to data that describe their
customers' buying habits, gender, age, income level, etc.
These companies tailor their marketing and product
development strategies to each consumer group to increase
sales and build brand loyalty.

5. The two-step clustering algorithm is designed to analyse
large databases as primary purpose. This algorithm
groups the observations in the clusters using the trait
approach.
Two step cluster analysis has an ability to create clusters
based on both categorical and continuous variables.

6. In the 1st step system creates a cluster tree with 1st case at
its root.
In the 2nd step it goes on creating the agglomeration of the
cases.

7. Age (year) Experience (Year) Designation Education (B.F.Sc,
M.F.Sc, P.Hd)
25 1 1 1
28 2.5 1 2
33 4 2 3
24 1 1 1
27 1.5 1 2
35 3.4 3 2
37 4.5 3 3
26 2 1 1
28 2 1 2
40 12 2 3
45 14.5 3 3
47 17 3 3
31 9.5 2 2
33 9 2 2
36 5.5 2 3
28 2.5 1 2
29 4.5 2 2
28 4 1 2
24 2 1 1
32 3.5 2 2
36 6.5 2 3
37 5.5 2 3
39 6.5 2 3
50 14 3 3
55 17 3 3
47 12 3 3
43 10 3 3
46 12.5 3 3
38 7.5 2 2
27 2.5 1 1

8.  In my example
I have used
Designation
and Education
is categorical
variables.
 And Age and
Experience are
continuous
variables.

12.  Here we can see the system has form 3
clusters.
 1st has 10 people, 2nd has 9 and the 3rd group
has 11 people

13.  By viewing the centroid
table it’s clear that
cluster 1 has less aged
and less experienced
people than cluster 3
 In Education Cluster 1
having more B.F.Sc
people and less M.F.Sc
people and no P.Hd
people; Cluster 2 having
more P.Hd people and
very less M.F.Sc People;
and in Cluster 3 we
have almost equal
M.F.Sc and P.Hd people

14.  So, its understandable is that 1st cluster is having less age, less designation, less
qualification people,
 If we come to the 3rd cluster they are more aged more educated and more designated
people.

15.  We can see that cluster
quality is good.
 3 cluster is formed total
number of variables
used is 4.