group 5 cluster analysis

8/6/2019 Group 5 Cluster Analysis

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CLUSTER ANALYSIS

PRESENTED BY:-

Garima Anand(34)

Sarabjeet kour(44)

Supriya koul(59)

Priyanshu Gupta(60)


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CONCEPT

Cluster analysis is a class of statistical techniques Cluster analysis is an exploratory data analysis tool.

Cluster analysis sorts through the raw data.

Acluster is a group of relatively homogeneous cases

or observations. Cluster analysis, is an interdependence technique.

Cluster analysis reduces the number of observations

or cases.

Example: A group of diners sharing the same table in a

restaurant may be regarded as a cluster of people. In

food stores items of similar nature, such as different

types of meat or vegetables are displayed in the same or

nearby locations.


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HYPOTHETICAL EXAMPLE

No. of vacation days

I

II

III

Expenditure on vacations(Rs.)

Vacations by 15 individuals(A To O ).

Three differentclusters ( I, II & III ) .

.C .M

.H

.F .L.N

.O .K

.G .D

.I

.A

.B .E

.J

To classify individuals or objects on the basis of their similarity

or distance from each other . Distance is in inverse measure of

similarity .


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BASIC PROCEDURE OF CLUSTER ANALYSIS

1. Formulate the problem .

2. Select a distance measure :

y Squared Euclidean distance .

y Manhattan distance .

y Chebyshev distance .

y Mahalanobis (or correlation) distance .

3. Select a clustering procedure .4. Decide on the number of clusters .

5. Map and interpret clusters (draw conclusions ).

6. Assess reliability and validity :

y Repeat analysis but use different distance measure .

y Repeat analysis but use different clustering technique .

y Split the data randomly into two halves and analyze each partseparately .

y Repeat analysis several times, deleting one variable each time

y Repeat analysis several times, using a different order eachtime .


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Clustering Methods

Clustering methods are categorized as:

Non-Hierarchicalclustering

Hierarchicalclustering


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Cont..

Non-Hierarchicalclustering:

first determine a cluster center,

then group all objects that are within a certain distance


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Examples

o Sequential Threshold method - first determine a cluster center, then

group all objects that are within a predetermined threshold from the

center.Only One cluster is created at a time.

o Parallel Threshold method - several cluster centers are determined

simultaneously, then objects that are within a predetermined threshold

from the centers are grouped.

o Optimizing Partitioning method - first a non-hierarchical procedure isrun, then objects are reassigned so as to optimize an overall criterion

o Centroid methods - clusters are generated that maximize the distance between the

centers of clusters (a centroid is the mean value for all the objects in the cluster)

o Variance methods - clusters are generated that minimize the within-cluster

variance


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Cont..

Hierarchicalclustering

objects are organized into an hierarchical

structure as part of the procedure

Examples:a) Divisiveclustering - start by treating all objects as if they are

part of a single large cluster, then divide the cluster into smaller

and smaller clusters.

b) Agglomerativeclustering - start by treating each object as a

separate cluster, then group them into bigger and bigger clustersexamples:


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Cont..

c) Linkage methods cluster of objects are based on the distance

between them

examples:

o Single Linkage method - cluster objects based on the minimum

distance between them (also called the nearest neighbour rule)

o Complete Linkage method - cluster objects based on the maximum

distance between them (also called the furthest neighbour rule)

o Average Linkage method - cluster objects based on the average

distance between all pairs of objects (one member of the pair must be from

a different cluster)


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ADVANTAGES Of CLUSTER

ANALYSIS IN MARKETING:-

Market segmentation

Buyer Behavior

Development Of New Product

Reduce Number Of Test Markets


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Disadvantage:-

Lack Of Specificity

Lack Specific Technique

Time Consuming


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group 5 cluster analysis

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