Transcript Lesson 6: Data Mining

IS 495 Summer 2015
Business Intelligence, Data Mining
and
Data Analytics/Predictive Analytics
By: Asela Thomason
What is Business Intelligence
• Basic Definition :Information that people use to
support their decision making efforts. Data
Mining and Data Analytics/predictive Analytics
falls within this field.
What is Data Mining
Data Mining (The analysis step of Knowledge
Discovery in Databases” Process or KDD), an
interdisciplinary subfield of computer Science, is
the computational process of discovering patterns
in large data sets involving methods at the
intersection of artificial intelligence, machine
learning, statistics, and database management
systems.
Basic-Definitions of Data Mining
• The discovery of new, non-obvious, valuable information from
a large collection of raw data
•
Data Mining (DM) is the core of the KDD [Knowledge Discovery in
Databases] process, involving the inferring of algorithms that explore
the data, develop the model and discover previously unknown
patterns.
• The set of activities used to find new, hidden or unexpected
patterns in data
Data Mining -continued
The overall goal of the data mining process is to
extract information from a data set and transform it
into an understandable structure for further use:
(Predictive analytics)
Discovering meaningful new corrections, patterns,
trends.
Example : Forecasting
Data Analytics/Predictive Analytics
Data analytics (DA) is the science of
examining raw data with the purpose of
drawing conclusions about that information.
Data analytics is used in many industries to
allow companies and organization to make
better business decisions and in the sciences
to verify or disprove existing models or
theories
Data analytics is distinguished from Data
mining by the scope, purpose and focus of the
analysis. Data miners sort through huge data sets
using sophisticated software to identify
undiscovered patterns and establish hidden
relationships. Data analytics focuses on inference,
the process of deriving a conclusion based solely
on what is already known by the researcher.
Predictive analytics - Focus
Uses lower level of Granularity, meaning it looks at
the individual level. Instead of looking at which
candidate will win the Presidential election in the
state of Ohio, which is forecasting. It looks at the
individual level.
Which person is voting for or against.
Predicts which individuals can be persuaded, which
ones will not change, etc. Now with this
information we ca change the outcome of the race.
Obama used this technique very well.
Data Mining: Business
• What is it?
• Decision making
• Marketing
• Detecting Fraud
• This technology is popular with many businesses
because it allows them to learn more about their
customers, prevent frauds and identity theft, and also
make smart marketing decisions
Keys to a Successful Data Mining Project
• Credible source of data
• Knowledgeable personnel
• Appropriate algorithms
Primary Tasks of Data Mining
Classification
classify a data item into one of
several predefined classes
Regression
map a data item to a real-value
prediction variable
Clustering
identify a finite set of
categories or clusters to
describe the data
find a compact description for a
set (or subset) of data
Summarization
Dependency Modeling
describe significant dependencies
between variables or between the
values of a feature
Change and Deviation
Detection
Discover the most significant
changes
Some of the commonly used data
mining methods are:
• Statistical Data Analysis
• Cluster Analysis
• Decision Trees and Decision Rules
• Association Rules
• Artificial Neural Networks
• Genetic Algorithms
• Fuzzy Sets and Fuzzy Logic
Data Mining Applications
In direct marketing a company saves much time
by marketing to prospects that would have the
highest reply rate. Instead of random selection on
which customers to pick for their surveys, a
company could use direct marketing from data
mining to find the “correct” customers to ask.
Direct Marketing-Example 1 million mailers- cost
$.40 to ship letter=400,000 cost
Conversion is 1percent without data mining
Direct Marketing using data mining, gives us 3%
Conversion
• Identifies smaller group, example ¼ of
population and gets a higher conversion, 3% ,
Data Mining Applications
Market segmentation is used in data mining in
order to identify the common characteristics of
customers who buy the products from one’s
company.
With market segmentation, you will be able to
find behaviors that are common among your
customers. As a company seeks customer’s trends,
it helps them find necessities in order to help them
improve their business.
Data Mining Applications
Customer churn predicts which
customers will have a change of heart
towards your company and join another
company (competitor). Although
customer churns are negative to one’s
business, it allows the corporation to seek
out the problem they are facing and create
solutions.
Customer Churn
• Example: Magazine subscriber
• Ideas to keep customer:
• Discount, coupons, etc.
Data Mining Applications
Market basket analysis- involves researching
customer characteristics in respect to their
purchase patterns
Example: Ralphs Club Card
Cereal and Milk
Market Basket
• Beer and diapers
• merchandising
Prediction based on Data
mining/Predictive analysis
• Examples of real life.
• Target – can predict which customers will be
pregnant
• Hospitals can predict which payments may need
to be admitted
• Credit card – can predict which customers may
miss their payment based upon where card is
used. Example Bar-alcohol=missed payments
Class Identification
• Mathematical taxonomy
• Concept clustering
Data Mining Applications
Class identification, which consists of
mathematical taxonomy and concept clustering.
Mathematical taxonomy focuses on what makes the
members of a certain class similar, as opposed to
differentiating one class from another.
For example, Ralphs can classify its customers based
on their income or past purchases
Data Mining Applications
Concept clustering - determines clusters according to
attribute similarity.
Consider the pattern a purchase of toys for age group 3–5
years, is followed by purchase of kid’s bicycle within 6
months about 90% of the time by high income customers,
which was discovered by data mining. The Company can
identify the prospective customers for kid’s bicycle based
on toy purchase details and adjust the mail catalog
accordingly.
Data mining Applications
Deviation analysis, A deviation can be fraud or a
change. In the past, such deviations were difficult
to detect in time to take corrective action. Data
mining tools help identify such deviations .
For example, a higher than normal credit purchase
on a credit card can be a fraud, or a genuine
purchase by the customer. Once a deviation has
been discovered as a fraud, the company takes
steps to prevent such frauds and initiates
corrective action
NetFlix
• Example covered in class.