The document provides an overview of cluster analysis, detailing its basic concepts, applications, and criteria for effective clustering. It describes clustering as a method of grouping similar data objects while distinguishing them from different groups across various fields such as biology, marketing, and climate. Additionally, it outlines the types of data used in cluster analysis, including interval-scaled, binary, nominal, ordinal, and mixed variables.

1. Cluster Analysis
Dr. Revathy V R
Assistant Professor
Dept. of Computer Science (UG)
Kristu Jayanti College, Bangalore

2. OVERVIEW
● Cluster Analysis: Basic Concepts
● Applications
● What is a good clustering?
● Types of Data in Cluster Analysis

3. Cluster Analysis: Basic Concepts
What is Cluster Analysis?
• Cluster: A collection of data objects.
– similar (or related) to one another within the same group. – dissimilar (or
unrelated) to the objects in other groups.
• Cluster analysis (or clustering, data segmentation, …)
– Finding similarities between data according to the characteristics found in
the data and grouping similar data objects into clusters.
• Clustering is a Unsupervised learning Concepts.
– As a stand-alone tool to get insight into data distribution.
– As a preprocessing step for other algorithms.

4. Applications of Clustering
• Biology: Taxonomy of living things like kingdom, phylum, class,
order, family, genus and species.
• Information retrieval: To document clustering.
• Land use: Identification of areas of similar land use in an earth
observation database.
• Marketing: Help marketers discover distinct groups in their
customer bases, and then use this knowledge to develop targeted
marketing programs.
• Climate: Understanding earth climate, find patterns of atmospheric
and ocean.
• Economic Science: Used for Market Research.

5. What Is Good Clustering?
• A good clustering method will produce high quality clusters.
– High intra-class similarity: cohesive within clusters.
– Low inter-class similarity: distinctive between clusters.
• The quality of a clustering method depends on :
– The similarity measure used by the method.
– Its implementation, and
– Its ability to discover some or all of the hidden patterns.

6. Types of Data in Cluster Analysis
1. Interval-Scaled variables
2. Binary variables
3. Nominal, Ordinal, and Ratio variables
4. Variables of mixed types

7. 1. Interval-Scaled variables :
Interval-scaled variables are continuous
measurements of a roughly linear scale.
Example:
weight and height, latitude and longitude
coordinates (e.g., when clustering houses),
and weather temperature.

8. 2. Binary variables :
A binary variable is a variable that can take
only 2 values.
Example : Generally gender variables can take 2
variables male and female.

9. 3. Nominal, Ordinal, and Ratio variables:
Nominal (or) Categorical variables
A generalization of the binary variable in that it can take more than
2 states.
Example : red, yellow, blue, green.
Ordinal Variables:
An ordinal variable can be discrete or continuous.
Example : Rank.
Ratio variables :
It is a positive measurement on a nonlinear scale, approximately at an
exponential scale.
Example : Ae^Bt or A^e-Bt.

10. 4.Variables of mixed types :
A database may contain all the six types of
variables symmetric binary, asymmetric
binary, nominal, ordinal, interval, and
ratio. Those combinedly called as mixed-
type variables.