Transcript Outputs Background Business objectives and success criteria

Issues in Data Mining
Infrastructure
Authors:
Nemanja Jovanovic, [email protected]
Valentina Milenkovic, [email protected]
Prof. Dr. Veljko Milutinovic, [email protected]
http://galeb.etf.bg.ac.yu/~vm
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Data Mining in the Nutshell

Uncovering the hidden knowledge

Huge n-p complete search space

Multidimensional interface
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A Problem …
You are a marketing manager
for a cellular phone company

Problem: Churn is too high

Turnover (after contract expires) is 40%

Customers receive free phone (cost 125$)
with contract

You pay a sales commission of 250$ per contract

Giving a new telephone to everyone
whose contract is expiring
is very expensive (as well as wasteful)

Bringing back a customer after quitting
is both difficult and expensive
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… A Solution

Three months before a contract expires,
predict which customers will leave

If you want to keep a customer
that is predicted to churn,
offer them a new phone

The ones that are not predicted to churn
need no attention

If you don’t want to keep the customer, do nothing

How can you predict future behavior?

Tarot Cards?

Magic Ball?

Data Mining?
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Still Skeptical?
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The Definition
The automated extraction
of predictive information
from (large) databases

Automated

Extraction

Predictive

Databases
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History of Data Mining
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Repetition in Solar Activity

1613 – Galileo Galilei

1859 – Heinrich Schwabe
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The Return of the
Halley Comet
Edmund Halley (1656 - 1742)
1531
1607
1682
239 BC
1910
1986
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2061 ???
Data Mining is Not

Data warehousing

Ad-hoc query/reporting

Online Analytical Processing (OLAP)

Data visualization
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Data Mining is

Automated extraction
of predictive information
from various data sources

Powerful technology
with great potential to help users focus
on the most important information
stored in data warehouses
or streamed through communication lines
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Data Mining can

Answer question
that were too time consuming
to resolve in the past

Predict future trends and behaviors,
allowing us to make proactive,
knowledge driven decision
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Focus of this Presentation

Data Mining problem types

Data Mining models and algorithms

Efficient Data Mining

Available software
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Data Mining
Problem Types
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Data Mining Problem Types

6 types

Often a combination solves the problem
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Data Description and
Summarization

Aims at concise description
of data characteristics

Lower end of scale of problem types

Provides the user an overview
of the data structure

Typically a sub goal
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Segmentation

Separates the data into
interesting and meaningful
subgroups or classes

Manual or (semi)automatic

A problem for itself
or just a step
in solving a problem
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Classification

Assumption: existence of objects
with characteristics that
belong to different classes

Building classification models
which assign correct labels in advance

Exists in wide range of various application

Segmentation can provide labels
or restrict data sets
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Concept Description

Understandable description
of concepts or classes

Close connection to both
segmentation and classification

Similarity and differences
to classification
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Prediction (Regression)

Finds the numerical value
of the target attribute
for unseen objects

Similar to classification - difference:
discrete becomes continuous
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Dependency Analysis

Finding the model
that describes significant dependences
between data items or events

Prediction of value of a data item

Special case: associations
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Data Mining Models
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Neural Networks

Characterizes processed data
with single numeric value

Efficient modeling of
large and complex problems

Based on biological structures
Neurons

Network consists of neurons
grouped into layers
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Neuron Functionality
I1
W1
I2
W2
I3
W3
In
f
Output
Wn
Output = f (W1*I1, W2*I1, …, Wn*In)
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Training Neural Networks
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Neural Networks - Conclusion

Once trained, Neural Networks
can efficiently estimate value
of output variable for given input

Neurons and network topology
are essentials

Usually used for prediction
or regression problem types

Difficult to understand

Data pre-processing often required
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Decision Trees

A way of representing a series of rules
that lead to a class or value

Iterative splitting of data
into discrete groups
maximizing distance between them
at each split

Classification trees and regression trees

Univariate splits and multivariate splits

Unlimited growth and stopping rules

CHAID, CHART, Quest, C5.0
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Decision Trees
Balance>10
Age<=32
Married=NO
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Balance<=10
Age>32
Married=YES
Decision Trees
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Rule Induction

Method of deriving a set of rules
to classify cases

Creates independent rules
that are unlikely to form a tree

Rules may not cover
all possible situations

Rules may sometimes
conflict in a prediction
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Rule Induction
If balance>100.000
then confidence=HIGH & weight=1.7
If balance>25.000 and
status=married
then confidence=HIGH & weight=2.3
If balance<40.000
then confidence=LOW & weight=1.9
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K-nearest Neighbor and
Memory-Based Reasoning (MBR)

Usage of knowledge
of previously solved similar problems
in solving the new problem

Assigning the class to the group
where most of the k-”neighbors” belong

First step – finding the suitable measure
for distance between attributes in the data

How far is black from green?

+ Easy handling of non-standard data types

- Huge models
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K-nearest Neighbor and
Memory-Based Reasoning (MBR)
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Data Mining Models
and Algorithms

Many other available models and algorithms

Logistic regression

Discriminant analysis

Generalized Adaptive Models (GAM)

Genetic algorithms

Etc…

Many application specific variations
of known models

Final implementation usually involves
several techniques

Selection of solution that match best results
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Efficient Data Mining
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NO
YES
Is It Working?
Don’t Mess With It!
YES
Did You Mess
With It?
You Shouldn’t Have!
NO
Anyone Else
Knows?
NO
YES
You’re in TROUBLE!
NO
Hide It
Can You Blame
Someone Else?
YES
NO PROBLEM!
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YES
Will it Explode
In Your Hands?
NO
Look The Other Way
DM Process Model

5A – used by SPSS Clementine
(Assess, Access, Analyze, Act and Automate)

SEMMA – used by SAS Enterprise Miner
(Sample, Explore, Modify, Model and Assess)

CRISP–DM – tends to become a standard
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CRISP - DM

CRoss-Industry Standard for DM

Conceived in 1996 by three companies:
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CRISP – DM methodology
Four level breakdown of the CRISP-DM methodology:
Phases
Generic Tasks
Specialized Tasks
Process Instances
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Mapping generic models
to specialized models

Analyze the specific context

Remove any details not applicable to the context

Add any details specific to the context

Specialize generic context according to
concrete characteristic of the context

Possibly rename generic contents
to provide more explicit meanings
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Generalized and Specialized
Cooking

Preparing food on your own

Raw
Find
out what
youvegetables?
want to eat
stake
with


Find the recipe for that meal
Check the Cookbook or call mom
Gather the ingredients
Defrost the meat (if you had it in the fridge)
Prepare the meal
Buy missing ingredients
Enjoy
yourthe
food
or borrow
from the neighbors
Clean up everything (or leave it for later)
Cook the vegetables and fry the meat

Enjoy your food or even more

You were cooking
so convince someone else to do the dishes







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CRISP – DM model

Business understanding

Data understanding

Data preparation

Modeling
Business
understanding
Deployment

Evaluation

Deployment
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Evaluation
Data
understanding
Data
preparation
Modeling
Customizing a Web Page

User-friendly design

Prediction of the users interests

Reduction of server workload

Reduction of Web traffic
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Customizing a Web Page
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Business Understanding

Determine business objectives

Assess situation

Determine data mining goals

Produce project plan
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Business Understanding - Outputs

Background

Business objectives and success criteria

Inventory of resources

Requirements, assumptions, and constrains

Risks and contingencies

Terminology

Costs and benefits

Data mining goals and success criteria

Project plan

Initial assessment of tools and techniques
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Customizing a Web Page –
Business Understanding Example


Business objectives
Make the users surfing
Assess
situation
more comfortable

Make the users
Decrease
of overhead
surfingfor users
 Data
mining
goals
more
comfortable
 Reduction of workload and
 Find
Web the
Decrease
traffic
patterns
of overhead for users
 Project
planbehavior
in the user
 Reduction of workload and
Web traffic

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Data Understanding

Collect initial data

Describe data

Explore data

Verify data quality
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Data Understanding - Outputs

Data collection report

Data

Background
description
of datareport


List of data sources
Data

Detailed
exploration
descriptionreport
of each data source


For each data source, method of acquisition

List of tables or other database objects
Data

Expected
quality
regularities
report or patterns and

Problems
encountered
in data acquisition
methods
of detection

Description
of
each
field
units, codes, etc.
Approach taken to assessincluding
data quality


Regularities or patterns found
Results of data quality assessment
(expected and unexpected)

Any other surprises

Conclusions for data transformation, data cleaning and
any other pre-processing

Conclusions related to data mining goals or
business objectives
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Customizing a Web Page –
Data Understanding Example




Collecting the data
Update the server to monitor
Data
userdescription
behavior

Record the users activities
Results
of data exploring
into a storage


Analyze recorded data

Decide which data is usable
for mining
Verification of the quality of the data
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Data Preparation

Select data

Clean data

Construct data

Integrate data

Format data
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Data Preparation - Outputs


Dataset description report

Background including broad goals and
plan for pre-processing

Description of pre-processing

Detailed description of resultant datasets

Rational for inclusion/exclusion of attributes

Discoveries made during pre-processing
and implications for further work
Dataset
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Customizing a Web Page –
Data Preparation Example

Decide from what period will the users
monitored actions be considered

Make assumptions about
unnecessary monitored data
and discard them

Classify user actions into categories,
group interesting links, etc…

If more information about user is available
from other sources, use them

Transform data into suitable forms
so several modeling techniques
could be applied
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Modeling

Select modeling technique

Generate test design

Build model

Assess model
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Modeling - Outputs

Assessment of DM results with respect to
business success criteria
Test design


Broaddescription
description of the type of model and
Model


the training data to be used

Type assessment
of model and relation to data mining goals
Model

Explanation of how the model will be tested or assessed

Overview
assessment
including
Parameterofsettings
used process
to produce
model

Description
of any
for testing
deviations from
thedata
testrequired
plan

Detailed description of the model and

Description
of any planned
of models
Detailed
assessment
of the examination
model
any special
features
by domain or data experts

Comments
models by
domaininorthe
data
experts
Conclusionson
regarding
patterns
data

Insights into why a certain modeling technique and
certain parameter setting lead to good/bad results
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Customizing a Web Page –
Modeling Example

The problem is prediction of behavior

Regression could be a good solution
due to distinct nature of the data

Create the software
according to the project plan

Observe the behavior of the software

Tune the model after each evaluation phase
if needed
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Evaluation
results = models + findings

Evaluate results

Review process

Determine next steps
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Evaluation - Outputs

Assessment of DM results with respect to
business success criteria


Reviewof
of process
Business Objectives and
Review

List
of possible
actions

Comparison
between
success criterion and DM results
Business Success Criteria

Conclusion about achievability of success criterion
and suitability of data mining process

Review of “Project Success”

Are there new business objectives?
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Customizing a Web Page –
Evaluation Example

Observe the model behavior at work

Collect response from Beta testers

Check user satisfaction

Check server and network engagement

Classify results

Determine which parameter of the model
should be changed

Present new ideas and modifications

Step back into previous phases as needed
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Deployment

Plan deployment

Plan monitoring and maintenance

Produce final report

Review project
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Deployment - Outputs

Monitoring and maintenance plan

Final

Overview
report
of deployment results and indication

which of results may require updating
Summary of Business Understanding
Description
ofobjectives
how updating
be triggered
(background,
and will
success
criteria)
Description
how
updating
will be performed
Summary ofof
data
mining
process

Summary of data mining results

Summary of results evaluation

Summary of deployment and maintenance plan

Cost/benefit analysis

Conclusions for the business

Conclusions for future data mining


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Customizing a Web Page –
Deployment Example

Make the feature available to all users

Make plan for maintenance and user feedback

Analyze costs and benefits

Summarize the whole documentation

Summarize network and server
additional activity

Collect the new ideas

Award according to results

Leave space for upgrade
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At Last…
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Available Software
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Available Software
Discussion of
data mining vendors and software
is not included into this slide set
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Conclusions
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WWW.NBA.COM
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Se7en
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 CD – ROM 
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Credits
Anne Stern, SPSS, Inc.
Djuro Gluvajic, ITE, Denmark
Obrad Milivojevic, PC PRO, Yugoslavia
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References

Bruha, I., ‘Data Mining, KDD and Knowledge Integration:
Methodology and A case Study”,
SSGRR 2000

Fayyad, U., Shapiro, P., Smyth, P., Uthurusamy, R.,
“Advances in Knowledge Discovery and Data Mining”,
MIT Press, 1996

Glumour, C., Maddigan, D., Pregibon, D., Smyth, P.,
“Statistical Themes nad Lessons for Data Mining”,
Data Mining And Knowledge Discovery 1, 11-28, 1997

Hecht-Nilsen, R., “Neurocomputing”,
Addison-Wesley, 1990

Pyle, D., “Data Preparation for Data Mining”,
Morgan Kaufman, 1999

galeb.etf.bg.ac.yu/~vm

www.thearling.com

www.crisp-dm.com

www.twocrows.com

www.sas.com/products/miner

www.spss.com/clementine
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The END
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