3. Data Mining
What is Data Mining?
“The process of semi automatically analyzing large
databases to find useful patterns” (Silberschatz)
KDD – “Knowledge Discovery in Databases” (3)
“Attempts to discover rules and patterns from data”
Discover Rules Make Predictions
Areas of Use
Internet – Discover needs of customers
Economics – Predict stock prices
Science – Predict environmental change
Medicine – Match patients with similar problems
cure
4. Example of Data Mining
Credit Card Company wants to discover
information about clients from databases. Want to
find:
Clients who respond to promotions in “Junk Mail”
Clients that are likely to change to another
competitor
Clients that are likely to not pay
Services that clients use to try to promote
services affiliated with the Credit Card Company
Anything else that may help the Company
provide/ promote services to help their clients
and ultimately make more money.
5. Data Mining & Data
Warehousing
Data Warehouse: “is a repository (or archive) of
information gathered from multiple sources, stored
under a unified schema, at a single site.”
(Silberschatz)
Collect data Store in single repository
Allows for easier query development as a single
repository can be queried.
Data Mining:
Analyzing databases or Data Warehouses to discover
patterns about the data to gain knowledge.
Knowledge is power.
8. Classification
Classification: Given a set of items that have several
classes, and given the past instances (training
instances) with their associated class, Classification
is the process of predicting the class of a new item.
Therefore to classify the new item and identify to
which class it belongs
Example: A bank wants to classify its Home Loan
Customers into groups according to their response to
bank advertisements. The bank might use the
classifications “Responds Rarely, Responds
Sometimes, Responds Frequently”.
The bank will then attempt to find rules about the
customers that respond Frequently and Sometimes.
The rules could be used to predict needs of potential
customers.
9. Technique for Classification
Decision-Tree Classifiers
Job
Income
Job
Income Income
Carpenter
Engineer Doctor
Bad Good Bad Good Bad Good
<30K <40K <50K>50K >90K
>100K
Predicting credit risk of a person with the jobs specified.
10. Clustering
“Clustering algorithms find groups of items
that are similar. … It divides a data set so that
records with similar content are in the same
group, and groups are as different as possible
from each other. ” (2)
Example: Insurance company could use
clustering to group clients by their age,
location and types of insurance purchased.
The categories are unspecified and this is
referred to as ‘unsupervised learning’
11. Clustering
Group Data into Clusters
Similar data is grouped in the same cluster
Dissimilar data is grouped in the same cluster
How is this achieved ?
K-Nearest Neighbor
A classification method that classifies a point
by calculating the distances between the
point and points in the training data set. Then
it assigns the point to the class that is most
common among its k-nearest neighbors
(where k is an integer).(2)
Hierarchical
Group data into t-trees
12. Regression
“Regression deals with the prediction of a value,
rather than a class.” (1, P747)
Example: Find out if there is a relationship
between smoking patients and cancer related
illness.
Given values: X1, X2... Xn
Objective predict variable Y
One way is to predict coefficients a0, a1, a2
Y = a0 + a1X1 + a2X2 + … anXn
Linear Regression
14. Association Rules
“An association algorithm creates rules that
describe how often events have occurred
together.” (2)
Example: When a customer buys a hammer,
then 90% of the time they will buy nails.
15. Association Rules
Support: “is a measure of what fraction of the
population satisfies both the antecedent and the
consequent of the rule”(1, p748)
Example:
People who buy hotdog buns also buy hotdog sausages in
99% of cases. = High Support
People who buy hotdog buns buy hangers in 0.005% of
cases. = Low support
Situations where there is high support for the
antecedent are worth careful attention
E.g. Hotdog sausages should be placed in near hotdog buns
in supermarkets if there is also high confidence.
16. Association Rules
Confidence: “is a measure of how often the consequent
is true when the antecedent is true.” (1, p748)
Example:
90% of Hotdog bun purchases are accompanied by hotdog
sausages.
High confidence is meaningful as we can derive rules.
Hotdog bun Hotdog sausage
2 rules may have different confidence levels and
have the same support.
E.g. Hotdog sausage Hotdog bun may have a
much lower confidence than Hotdog bun Hotdog
sausage yet they both can have the same support.
17. Advantages of Data Mining
Provides new knowledge from existing data
Public databases
Government sources
Company Databases
Old data can be used to develop new knowledge
New knowledge can be used to improve services or
products
Improvements lead to:
Bigger profits
More efficient service
18. Uses of Data Mining
Sales/ Marketing
Diversify target market
Identify clients needs to increase response rates
Risk Assessment
Identify Customers that pose high credit risk
Fraud Detection
Identify people misusing the system. E.g. People
who have two Social Security Numbers
Customer Care
Identify customers likely to change providers
Identify customer needs
20. Privacy Concerns
Effective Data Mining requires large sources of data
To achieve a wide spectrum of data, link multiple data
sources
Linking sources leads can be problematic for privacy as
follows: If the following histories of a customer were
linked:
Shopping History
Credit History
Bank History
Employment History
The users life story can be painted from the collected
data
21. References
1. Silberschatz, Korth, Sudarshan, “Database System
Concepts”, 5th
Edition, Mc Graw Hill, 2005
2. http://www.twocrows.com/glossary.htm, “Two Crows,
Data Mining Glossary”
3. http://en.wikipedia.org/wiki/Data_mining, “Wikipedia”
4. http://phoenix.phys.clemson.edu/tutorials/excel/regres
sion.html
5. http://wwwmaths.anu.edu.au/~steve/pdcn.pdf