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Data Mining - Introduction, Study notes of Data Mining

Moradabad Institute of Technology Data Mining

Detail Summery about Cluster Analysis, Outlier Analysis, Grid-Based Methods, Summary , Clustering High-Dimensional Data , Model-Based Methods.

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November 27, 2014

Data Mining: Concepts and

Techniques 1

Cluster Analysis

1. What is Cluster Analysis?

2. Types of Data in Cluster Analysis

3. A Categorization of Major Clustering Methods

4. Partitioning Methods

5. Hierarchical Methods

6. Density-Based Methods

7. Grid-Based Methods

8. Model-Based Methods

9. Clustering High-Dimensional Data

10.Constraint-Based Clustering

11.Outlier Analysis

12.Summary

Partial preview of the text

Download Data Mining - Introduction and more Study notes Data Mining in PDF only on Docsity!

Data Mining: Concepts and

Cluster Analysis

1. What is Cluster Analysis?

2. Types of Data in Cluster Analysis

3. A Categorization of Major Clustering Methods

4. Partitioning Methods

5. Hierarchical Methods

6. Density-Based Methods

7. Grid-Based Methods

8. Model-Based Methods

9. Clustering High-Dimensional Data

10.Constraint-Based Clustering

11.Outlier Analysis

12.Summary

Data Mining: Concepts and

What is Cluster Analysis?

 Cluster : a collection of data objects

 Similar to one another within the same cluster

 Dissimilar to the objects in other clusters

 Cluster analysis

 Finding similarities between data according to the

characteristics found in the data and grouping similar

data objects into clusters

 Unsupervised learning: no predefined classes

 Typical applications

 As a stand-alone tool to get insight into data distribution

 As a preprocessing step for other algorithms

(characterization, attribute subset selection,

classification )

Data Mining: Concepts and

Examples of Clustering

Applications

 Marketing: Help marketers discover distinct groups in their

customer bases, and then use this knowledge to develop

targeted marketing programs

 Land use: Identification of areas of similar land use in an

earth observation database

 Insurance: Identifying groups of motor insurance policy

holders with a high average claim cost

 City-planning: Identifying groups of houses according to

their house type, value, and geographical location

 Earth-quake studies: Observed earth quake epicenters

should be clustered along continent faults

Data Mining: Concepts and

Quality: What Is Good

Clustering?

 A good clustering method will produce high quality

clusters with

high intra-class similarity

 low inter-class similarity

 The quality of a clustering result depends on both the

similarity measure used by the method and its

implementation

 The quality of a clustering method is also measured

by its ability to discover some or all of the hidden

patterns

Data Mining: Concepts and

Data Mining - Introduction, Study notes of Data Mining

Related documents

Partial preview of the text

Download Data Mining - Introduction and more Study notes Data Mining in PDF only on Docsity!

Cluster Analysis

1. What is Cluster Analysis?

2. Types of Data in Cluster Analysis

3. A Categorization of Major Clustering Methods

4. Partitioning Methods

5. Hierarchical Methods

6. Density-Based Methods

7. Grid-Based Methods

8. Model-Based Methods

9. Clustering High-Dimensional Data

10.Constraint-Based Clustering

11.Outlier Analysis

12.Summary

What is Cluster Analysis?

 Cluster : a collection of data objects

 Similar to one another within the same cluster

 Dissimilar to the objects in other clusters

 Cluster analysis

 Finding similarities between data according to the

characteristics found in the data and grouping similar

data objects into clusters

 Unsupervised learning: no predefined classes

 Typical applications

 As a stand-alone tool to get insight into data distribution

 As a preprocessing step for other algorithms

(characterization, attribute subset selection,

classification )

Examples of Clustering

Applications

 Marketing: Help marketers discover distinct groups in their

customer bases, and then use this knowledge to develop

targeted marketing programs

 Land use: Identification of areas of similar land use in an

earth observation database

 Insurance: Identifying groups of motor insurance policy

holders with a high average claim cost

 City-planning: Identifying groups of houses according to

their house type, value, and geographical location

 Earth-quake studies: Observed earth quake epicenters

should be clustered along continent faults

Quality: What Is Good

Clustering?

 A good clustering method will produce high quality

clusters with

high intra-class similarity

 low inter-class similarity

 The quality of a clustering result depends on both the

similarity measure used by the method and its

implementation

 The quality of a clustering method is also measured

by its ability to discover some or all of the hidden

patterns

Requirements of Clustering in Data

Mining

 Scalability

 Ability to deal with different types of attributes

 Ability to handle dynamic data

 Discovery of clusters with arbitrary shape

 Minimal requirements for domain knowledge to

determine input parameters

 Able to deal with noise and outliers

 Incremental clustering and Insensitive to order of input

records

 High dimensionality

 Incorporation of user-specified constraints

 Interpretability and usability