Download Data Mining

Data Mining
By Jason Baltazar, Phil Cademas, Jillian
Latham, Rachel Peeler & Kamila Singh
What is Data Mining?

Data Mining is data processing using
sophisticated data search capabilities
and statistical algorithms to discover
patters and correlations in large
preexisting databases.

2 Broad Categories:

Supervised & Unsupervised
Unsupervised Data Mining

“Descriptive Modeling”

Uncover patterns and relationships
among data

No predetermined parameters

Observations after analysis

Used to assist in making business
decisions
Cluster Analysis

“Automated Data Mining”

Used to discover the segments or groups
within a customer data set

Determine classes of similar customers that
naturally fit together

Demographics

Segmented Markets

Marketing and Advertising
Supervised Data Mining

“Predictive Modeling”

Set goals and parameters prior to data
mining

Concentration: only relevant patterns

Predict outcomes

Anomaly Detection, Classification &
Prediction, Regression, Analysis
Anomaly Detection

Models built to specify “normal” ranges of
results

Fraud Detection

Tax, insurance, credit card industries

Prevent Identity Theft

Detect breaches in computer security

PayPal

15% of all e-commerce in the U.S.
Classification & Prediction

Most common data analysis tool

“Who will buy what, and how much will they
buy?”

Credit analysis / Credit Scoring – Who are
my “good credit risks?”

Based on spending habits, income, and/or
demographics

Can be used in customer segmentation,
business modeling, credit analysis, etc.
Classification & Prediction

Human Resources

Turnover analysis, employee development,
recruiting, training, and employee retention



Determine the “value” of employees
Fill leadership/management positions from within
the organization
Groom and promote based on a set of
predetermined skills, attitudes, and
competencies
Regression Analysis

Statistics applies to data to make
predictions
 i.e.
How product price and promotions
affect sales

Marketing, pricing, product positioning,
sales forecasting, advertising, human
relations, customer service

Objectives: market response modeling
and sales forecasting
Text Mining

Text Mining is the process of
automatically processing text and
extracting information from it

Presidential election
Text Mining Applications

Security Applications

Biomedical Applications

Online Media Applications

Academic Applications
Data Mining Advantages

Helps to reduce costs

Provides improved and more detail
oriented service

Increases market effectiveness

Beneficial to all industries
Data Mining Disadvantages

Privacy Issues


Security Issues


Access to personal information
Insufficient security systems
Misuse of information & inaccurate
information
Insurance & Healthcare

Target marketing

Helps to develop different plans and
policies
Mobile Communication
Helps develop a
variety of different
cell phone plans


Target marketing
Data Mining Privacy

Who has access to consumer personal
information
 CVS
Pharmacy & Marketing Companies
Data Mining Ethics: Consumers

How far is too far?

Trustworthy?

Data is being collected & used

Opt out boxes

What are some solutions that give consumers
control?


Access to databases that have their information
The right to change what information is available
Data Mining Ethics: Businesses

Help enhance overall customer
satisfaction
 Profit
enhancer?
 Violation of privacy

Sometimes partnered with marketing
companies
 They
also have access to private
information
Conclusion
ANY QUESTIONS?