a look at data mining presented by: charles hollingsworth flavia peynado ritch overton dsc8020,...

A Look at Data Mining

Presented by:

Charles Hollingsworth

Flavia Peynado

Ritch Overton

DSc8020, Group Presentation, July 31, 2002

What is Data Mining?

It may be described as the process of extracting previously unidentified, valid, and actionable information from large databases and then using the information to make crucial business decisions.

Why the need for data mining?

Business environment is constantly changing. Customer Behavior Patterns Market Saturation New niche markets Increased commoditization Time to market Shorter product life cycles Increased competition and business risks

Drivers The Customer Products Competition Operations/Data

Assets.

Enablers Data flood Growth of data

warehousing New IT solutions New research in

machine learning

Process overview contd.

1. Business Understanding

2. Data understanding

3. Data Preparation

4. Data Transformation

5. Data Mining

6. Analysis of results

7. Assimilation of results

Effort needed at each stage of data mining

0

10

20

30

40

50

60

Effort

Visualization

Goal is to provide a summary and overview of a dataset

Promotes Understanding: Deconstructive process

Promotes Trust: Constructive process

Narrows the gap between human and computer during data analysis

Types of Visualization Tools

Histograms Bar Charts Scatter plots Pie Charts Line Plots

Time-Series Plots Decision Trees

Coxcomb Plots Stereograms Mosley’s X-ray’s

Histogram

Graphically illustrates how many observations fall in various categories

 Histogram for Diameter

0

20

40

60

80

100

Category

Bar Chart

Categories are placed on the vertical axis, instead of the horizontal axis in a histogram

Scatter Plot

Scatter Plot

0

5

10

15

20

25

0 50 100 150 200

Domestic Gross

Sa

lary

Salary

Graphical representation of the relationship between two variables.

Pie Chart

Radii are used to divide a circle into wedges. The resulting angles represent the values of the wedges.

Spring 2000 Salary Survey

<$30,000

$30,000 to $39,999

$40,000 to $49,999

$50,000 to $59,999

$60,000 to $69,999

More than $70,000

No Answer

Line Plot

Connects consecutive data points to enhance visualization

Time-Series Plot: Playfair’s

•Helpful in forecasting future values

•Time variable is placed on the horizontal axis

•Makes patterns in data more apparent

•The area between two time-series curves was emphasized to show the difference between them, representing the balance of trade.

Decision Trees

Conventions for Decision Trees:

1. Composed of nodes (points in time) and branches (possible decisions).

2. Squares represent decision nodes, circles represent probability nodes, triangles represent end nodes.

3. Probabilities are listed on probability branches.

4. Monetary values are listed on the branches where they occur.

5. Decision maker has no control over probability branches.

Decision Trees

Coxcomb Plot

In 1858, Florence Nightingale constructed graphs of her own design, which she called “Coxcombs".

The radii in a Coxcomb vary as opposed to the angle of the wedge in a pie chart.

Stereogram

Luigi Perozzo, from the Annali di Statistica, 1880

The population of Sweden from 1750-1875 by age groups

Mosley’s X-ray’s

Caused Henry Mosley to discover that the atomic number is more than a serial number; that it has some physical basis. Moseley proposed that the atomic number was the number of electrons in the atom of the specific element.

Other Visualization Tools

Doughnut Area Chart Box Plot Radar

Algorithms

Predictive

Regression Classification

Descriptive Parallel Formulation

of Classification Association Rule

Discovery Sequential Pattern

Discovery Analysis Clustering

Applying

Relevance to managers

Decreasing Costs Valuing Appropriately Effective Implementation

Conclusion

Converging Developments Data compilation Processing power Maturing Algorithms Visualization

Accessible Resources