An introduction to data mining process and three algorithms implementation.

1. EVALUATION AND VISUALIZATION OF
DIFFERENT DATA MINING TECHNIQUES
INTRODUCTION TO DATAMINING BY SUMAIRA S.

2. Data Mining Process
INTRODUCTION TO DATAMINING BY SUMAIRA S.

3. The purpose of this project is to gain an understanding of the process of data
mining by

Implementing one or more data mining algorithms

Visualizing them

Comparing their performance on datasets

Another aspect was to provide visual tutorials and detailed help about these algorithms
INTRODUCTION TO DATAMINING BY SUMAIRA S.

4. WHAT IS DATA MINING?
 Originally developed to act as expert systems to solve problems
 Data Mining can be utilized in any organization that needs to find
patterns or relationships in their data.
 Different types of Data Mining
INTRODUCTION TO DATAMINING BY SUMAIRA S.

5. BASIC FEATURES OF THE PROJECT
 Handling different types of data
 Pre processing of data
 Algorithms implementation
 Visualization of data mining model
 Comparison of different data mining algorithms
 Help and visual tutorials
INTRODUCTION TO DATAMINING BY SUMAIRA S.

6. HANDLING DIFFERENT DATA FORMATS
System supports following types of data files
 Text Data File Handling

CSV (Comma Separated Value) File

Any User Defined Format
 Database Data File Handling

MS Access Data File

MS SQL Data File
 XML Data File Handling

XML Data File
INTRODUCTION TO DATAMINING BY SUMAIRA S.

7. PRE PROCESSING OF DATA
 Pre processing of data includes

Filling of missing values

Ignore row
INTRODUCTION TO DATAMINING BY SUMAIRA S.

8. ALGORITHMS’ IMPLEMENTATION
 Clustering

Partitional Clustering Algorithm


K-Means Algorithm
Hierarchical Clustering Algorithms

Single Linkage Algorithm

Weighted Average Algorithm

Complete Linkage Algorithm
INTRODUCTION TO DATAMINING BY SUMAIRA S.

9. VISUALIZATION OF DATA MINING MODEL
 XYScatter Chart Visualization
 Dendrogram
 Pie Chart
 Curve Graph
INTRODUCTION TO DATAMINING BY SUMAIRA S.

10. COMPARISON OF DIFFERENT DATA
MINING ALGORITHMS
 Data File Comparison

Running time

Memory Usage

CPU Usage

Precision/Recall
INTRODUCTION TO DATAMINING BY SUMAIRA S.

11. K-MEAN ALGORITHM
 K-mean was introduced by MC Queen in 1967
INTRODUCTION TO DATAMINING BY SUMAIRA S.

12. THE K-MEANS CLUSTERING METHOD
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Assign
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Update
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Arbitrarily choose K
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cluster center
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reassign
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INTRODUCTION TO DATAMINING BY SUMAIRA S.
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reassign
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13. SINGLE LINKAGE HIERARCHICAL CLUSTERING
1. Say “Every point is
its own cluster”
2. Find “most similar”
pair of clusters
INTRODUCTION TO DATAMINING BY SUMAIRA S.

14. SINGLE LINKAGE HIERARCHICAL CLUSTERING
1. Say “Every point is
its own cluster”
2. Find “most similar”
pair of clusters
3. Merge it into a
parent cluster
INTRODUCTION TO DATAMINING BY SUMAIRA S.

15. SINGLE LINKAGE HIERARCHICAL CLUSTERING
1. Say “Every point is
its own cluster”
2. Find “most similar”
pair of clusters
3. Merge it into a
parent cluster
4. Repeat
INTRODUCTION TO DATAMINING BY SUMAIRA S.

16. SINGLE LINKAGE HIERARCHICAL CLUSTERING
1. Say “Every point is
its own cluster”
2. Find “most similar”
pair of clusters
3. Merge it into a
parent cluster
4. Repeat
INTRODUCTION TO DATAMINING BY SUMAIRA S.

17. THANK YOU
Presentation By:
Sumaira Sohail.
INTRODUCTION TO DATAMINING BY SUMAIRA S.