FROM BUSINESS OBJECTIVES TO DATA MINING: TOWARDS A SISTEMATIC WAY OF DATA MINING PROJECT DEVELOPMENT - PowerPoint PPT Presentation

About This Presentation
Title:

FROM BUSINESS OBJECTIVES TO DATA MINING: TOWARDS A SISTEMATIC WAY OF DATA MINING PROJECT DEVELOPMENT

Description:

Many algorithms have been implemented but no systematically proof of one better ... To map data mining algorithms to a conceptual model. Achievements of the model: ... – PowerPoint PPT presentation

Number of Views:182
Avg rating:3.0/5.0
Slides: 44
Provided by: scarmarb
Category:

less

Transcript and Presenter's Notes

Title: FROM BUSINESS OBJECTIVES TO DATA MINING: TOWARDS A SISTEMATIC WAY OF DATA MINING PROJECT DEVELOPMENT


1
FROM BUSINESS OBJECTIVES TO DATA MINING TOWARDS
A SISTEMATIC WAY OF DATA MINING PROJECT
DEVELOPMENT
  • Ernestina Menasalvas
  • Facultad de Informática
  • Universidad Politecnica de Madrid. Spain
  • emenasalvas_at_fi.upm.es
  • November 2004

2
Background(I)
  • 1995 doctoral student.
  • Visit University of Regina (Prof. Ziarko)
  • Visit Warsaw University (Prof. Pawlak)
  • 1998 Defend thesis. Data Mining process model
    (Anita Wasilewska C. Fernandez-Baizan)
  • Since then
  • Data Bases Professor Data bases, data mining
  • Coordinator of the Data Mining group at Facultad
    de Informática UPM
  • Techniques Rough Sets, Bayes,
  • Methodologies for data mining process management
  • Evaluation in Data Mining
  • Experimentation in Web Mining
  • Web Mining Web Goal Mining

3
Background(II)
  • Projects developed
  • Pure Research
  • Data Mining to be integrated on RDBMS
  • Web Profiler
  • Methodology for Data Mining process management
  • Research and application
  • Data Mining applied on different domains
  • Car dealers
  • Travel agency
  • .

4
Data Mining Project Development
  • Methodologies for Data Mining project development
  • Is it really Data Mining a Science?
  • Are we developing proyects as an art?
  • Has the research got the same results in all the
    areas??
  • Algorithms
  • Data Preparation
  • Data enrichment
  • Conceptualization of Data Mining problems

5
Data Mining an art, a science?
  • Since it appeared a lot of algorithms have been
    programmed
  • Standards
  • Crisp-DM
  • SEMMA
  • PMML 3.0
  • Process depends on the expertise of the data
    miner
  • User speaks about business problems
  • Data Miner speaks about algorithms

6
Data Mining as a project
  • Data Mining is data intensive activity
  • Data understanding
  • Data Preparation
  • Database manager
  • Transactional databases
  • Datawarehouses
  • The end result of a data mining project is a tool
    (software project) for better decision making
    process
  • Software development project
  • IT department has to be involved

7
Project Management
  • Why?
  • In order to organize the process of develpoment
    and to produce a project plan
  • How?
  • Establish how the process is going to be develop
  • Sequential
  • Incremental
  • What?
  • Establish how is the process is splitted into
    phases and define the tasks to be developed in
    each step
  • RUP
  • XP
  • COMMONKADS
  • Way of making things
  • Independent of the process being developed

LIFECYCLE MODELS
  • Particular tasks
  • Detail of tasks to be developed

METHODOLOGY
8
Common pitfall of data mining implementation
  • The common pitfall of data mining implementation
    the following
  • Not being able to efficiently communicate mining
    results within an organization.
  • Not having the right data to conduct effective
    analysis.
  • Not using existing data correctly.
  • Not being able to evaluate results
  • Questions that arise
  • Can the adequateness of a set of data for a
    problem be established when preparing the project
    plan?
  • How the set of data can be used to produce the
    expected results?
  • How we can evaluate the results?
  • Cost estimation?

9
Data Mining Approaches
  • Vendor independent
  • CRISP-DM
  • Based on the commercial tools
  • CATs
  • SEMMA
  • CRM Methodology
  • CRM Catalyst

Model Process
Not Real Methodology Based on Crisp-DM
Globlal CRM process Does not concentrate on Data
Mining step
10
Cross-Industry Standard Process for Data
MiningCRISP-DM
11
Data Mining as a project CATs
  • CATs Clementine Application Templates CATs
  • Specific libraries of best practices that provide
    inmediate value right out of the box
  • Following the CRISP-DM standard. Every CAT stream
    is assigned to a CRISP-DM phase
  • They provide long term value as they can always
    be used with a new data set for new insight in
    other projects.
  • Available as an add-on module to Clementine,
    include
  • Telco CAT - improve retention and cross-selling
    efforts for telecommunications
  • CRM CAT - understand and predict customer
    migration between segments,
  • Microarray CAT - accelerate biological
    discoveries, find genes Fraud CAT - predict and
    detect instances of fraud in financial
    transactions, claims, tax returns
  • Web CAT

12
What is a CAT?CATs
13
SEMMA(1)
  • SEMMA (Sample, Explore, Modify, Model, Assess)
    SEMMA
  • Is not a data mining methodology
  • Rather a logical organization of the functional
    tool set of SAS Enterprise Miner for carrying out
    the core tasks of data mining.
  • Enterprise Miner can be used as part of any
    iterative data mining methodology adopted by the
    client.
  • Naturally steps such as formulating a well
    defined business or research problem and
    assembling quality representative data sources
    are critical to the overall success of any data
    mining project.

14
SEMMA(2)
  • SEMMA is focused on the model development aspects
    of data miningSEMMA
  • Sample the data to extract a portion of a large
    data set big enough to contein significant
    information, yet small to manipulate quickly.
  • Explore the data by searching for anticipated
    trends and anomalies in order to gain
    understanding and ideas.
  • Modify the data by creating selecting and
    transforming the variables to focus the model
    selection problem.
  • Model the data allowing the software to search
    automatically for a combination of data that
    reliably predicts a desired outcome. Modelling
    techniques include neural networks,
    tree-clasiffiers, statistical models, etc.
  • Assess the data by evaluating the usefulness and
    reliability of the findings from the data mining
    process and estimate how well it performs.

15
Methods for Project ManagementCRM Catalyst(1)
  • Developed jointly by CustomISe, MACS and
    SalesPathways. Together they have formed the
    Catalyst Foundation http//www.crmmethodology.com/
  • Motivations
  • CRM projects are difficult to execute
    successfully because of the wide range of factors
    influencing their success. So it can take a long
    time to make CRM work properly for an
    organisation.
  • Solution CRM Catalyst.
  • Methodology acts as a catalyst for CRM projects
    enabling them to achieve their objectives more
    reliably and in less time.
  • It gives a project life cycle with a set of
    defined phases broken down into steps with
    clearly stated inputs and outputs.

16
Methods for Project Management CRM Catalyst(2)
Implementation requires Data Mining development
process
Progressive Lifecycle Model
The resutls are obtained in a progressive way
Implementation is Knowledge intensive
In some steps Knowledge Intensive Methdology
could be appropriate
17
Main steps in a Data Mining Project
  • Define the goals
  • Business and data mining experts together have to
    define the goals
  • Each goal must be defined with measurements for
    success
  • Obtain the models
  • Apply data mining algorithms.
  • Preprocesing is important
  • Evaluate results
  • ascertaine the value of an object according to
    specified criteria, operationalised in terms of
    measures.
  • Deploy
  • Decide patterns and models that can be deployed
  • Evaluate
  • After product working it should be contrasted the
    result

18
1. Define the goals
  • Distinguish between
  • Data Mining goals
  • Business goals
  • How do we translate?
  • Increase the lifetime value of valuable customers

?
?
?
Clasification
Estimation
Association
It has to be solved in the Business Understanding
step of CRISP-DM
19
Business Understandingin the CRISP-DM Process
Business Understanding
Business Success Criteria
Background
Business Objectives
Determine Business Objectives
Inventory Resources
Reqs, Assumptions Constraints
Risks Contingencies
Terminology
Costs Benefits
Assess Situation
Determine Data Mining Goals
Data Mining Goals
Data Mining Success Criteria
Produce Project Plan
Initial Assessment of Tools Techniques
Project Plan
20
1.1 Determine Business objectives and success
criteria
  • Not only business objectives have to be
    established but measures in order to be able to
    evaluate the results
  • Business objectives
  • What is the customer's primary objective?
  • Increase the number of loyal customers
  • Selling more of a certain product
  • Have a positive marketing campaing
  • Business success criteria
  • What constitutes a successful outcome of the
    project?
  • Objectives measures so that the success can be
    established
  • ROI

21
1.2 Costs Benefits
  • Perform a cost-benefits analysis
  • Compute the benefits of the project
  • Which measures do we have?
  • ROI
  • APEX
  • OPEX....
  • Compute the costs of the project (equipment,
    human resources...)
  • Which methodology do we have?
  • COCOMO for sortware
  • Quantify the risk that the project fails
  • Knowledge not available
  • Data Not available
  • Proper tools

22
Data Mining Estimation Model
  • Establishing a parametrical estimation model for
    Data Mining (Marban03)

DMCOMO (Data Mining COst MOdel)
23
Data Mining Cost Estimation
  • Main factors in a Data Mining project
  • Data Sources (number, kind, nature, )
  • Data mining problem to be solved (descriptive,
    predictive, )
  • Development platform
  • Available tools
  • Expertise of the development team
  • Drivers
  • Data Drivers
  • Model Drivers
  • Platform Drivers
  • Tools and techniques Drivers
  • Project Drivers
  • People Drivers

24
1.3 Data Mining goals and success
  • Data mining goals
  • Translate the customer's primary objective into a
    data mining goal, e.g.
  • Loyalty program translated into segmentation
    problem
  • Decreasing the attrition rate transformed into
    classification problem
  • Data mining success criteria
  • Determine success in technical terms
  • Translate the notion of sucess into confidence,
    support and lift and other parameteres
  • Determine de cost of errors
  • How do we make the translation?

25
Methodology
  • Which is the methodology to be followed to
    translate business objectives into data mining
    objectives?
  • Unluckily, there is no such methodology. First we
    have to solve
  • How a business objective is expressed?
  • What is a data mining goal?
  • How are data mining goals achieved?
  • Which are the requirements of data mining
    functions?

In order to describe everything in a standard
way Conceptualize the problem
26
Conceptualization in other disciplines
  • Data Bases
  • E/R diagrams
  • Independent of the domain
  • A tool for business understanding and for data
    base designer
  • Translation from E/R to implementation

External view n
External view 1
Conceptual Schema
Internal Schema
27
3 levels proposed architecture
Business problem
Business problem
Requirements of algorithms will be solved at
this level
Conceptual Schema
Internal Schema
Tools requirements to be solved
SAS, WEKA, Clementine
28
3 layers architecture for data mining
  • It is the bridge
  • Between business goals and the final tool
  • Independent of the domain
  • Provides independence
  • Changes in the tool do not reflect to the
    solution
  • It has to be decided what to model in the
    conceptualization
  • Automatic translation of business goals into data
    mining goals
  • Data Mining goals constraints feasible data
    mining goals

29
Elements to conceptualize
  • Elements to be taken into account
  • Data
  • Quality from data mining point of view
  • Adequateness for the problem
  • Classification for data mining purposes
  • Knowledge
  • Related to the process being analyzed
  • Related to the data used
  • People
  • Owners of data
  • Experts in the process
  • Data mining problems requirements
  • Data mining methods requirements

30
Proposed process
31
DMMO
  • Data Mining Modelling Objects
  • Data
  • Knowledge
  • Constraints of data and applications
  • Data Mining objects
  • Algorithms
  • Measures
  • Methods
  • To bridge the gap between data miners and
    business users

32
Are data adequate for analysis?
  • The adequateness of the data is analyzed taking
    into account goals to fulfil.
  • Data together with the knowledge extracted from
    the experts can be transformed so that just by
    being the input of a certain data mining
    algorithm will produce the required patterns.
  • Quality of the data, in this context
  • is not only related to the technical quality
    proper model, percentage of null values,
  • but also has to do with
  • meaning of the attributes,
  • Where each piece of data comes from,
  • relationship among data, and
  • finally how the data fulfil the requirements of
    the data mining functions

33
2. Data Mining obtain models
  • Apply data mining process model
  • Associated problems solved by the 3 layers
    architecture
  • Comparison of approaches
  • Evaluate costs
  • Pros and cons of approaches
  • Only experience or a conceptualization can help
  • The conceptual model will help to establish the
    process to obtain each feasible model.
  • Requirements and transformations implicit in the
    model

34
2.1 Determine type of problem
  • What are data mining problems?
  • Classification
  • Estimation
  • Association
  • Segmentation
  • In the conceptual model requirements for each
    type will be settled

35
2.2 Apply CRISP-DMprocess model
  • Data Mining problem has to be settled before
    going into modeling step
  • Requierements will be established in Business
    understanding
  • Requierements will be checked in Data
    Understanding and data Preparation
  • Preparation will be guided by conceptual model
  • Evaluation on feasibility can be done before
    applying the model

Business Understanding
Data Understanding
Data Preparation
Modeling
Evaluation
Deployment
Business Understanding
36
3. Evaluate results
  • Spilipopou, Berendt
  • Evaluation the act of ascertaining the value of
    an object according to specified criteria,
    operationalised in terms of measures.
  • Object model already obtained
  • Criteria and Measures and has to do with goals
  • Evaluation requires a well-defined notion of
    success, which must be in place before
  • the evaluation takes place
  • the data mining phase starts
  • any work with the data starts
  • i.e. already during the business understanding
    process.
  • Here once again conceptualization plays its role

37
Evaluation in the CRISP-DM Process
  • The CRISP-DM process is
  • a non-ending circle of iterations
  • a non-sequential process, where backtracking at
    previous phases is usually necessary
  • In each sequential instantiation evaluation takes
    place
  • But it is a cycle
  • In all the iterations all the steps should be
    revisited
  • Results have to be evaluated!!

Business Understanding
Data Understanding
Data Preparation
Modeling
Evaluation
Deployment
Business Understanding
38
4. Deployment
  • All the models that have possitive evaluation can
    be deployed
  • For measurements of success to trust deployment
    has to follow rules established at the beginning
    of the project
  • The real evaluation has not yet been performed

39
5. Evaluate after deployment
  • After deployment there is the need to proof that
    the improvements are really due to the actions
    taken after a data mining discovery and not to
    any other factor or action carried out in the
    company
  • None of the obvious claims about success of data
    mining have ever been systematically tested.
  • Experiments are crucial to establish if the
    impact of the deployment is really positive or
    negative
  • Experiments have to be designed at the beginning
    of the project

40
Conclusions
  • Data mining projects are being developed more as
    art than a science
  • Many algorithms have been implemented but no
    systematically proof of one better than another
    in real case is done after deployment
  • Conceptual model is required
  • To map business goals to the model
  • To map data mining algorithms to a conceptual
    model
  • Achievements of the model
  • Will be used along the process to guide the
    project
  • Evaluation tool

41
Future works
  • Conceptual model
  • Define DMMO objects
  • Evaluation techniques related to the model
  • Evaluate data mining goals
  • Evaluate business goals
  • Experimentation methods
  • obstursively and
  • non obstrusivelsly

42
References
  • Evaluation in Web mining Tutorial at ECML/PKDD
    2004 Pisa, Italy 20th September, 2004. Bettina
    Berendt, Myra Spiliopoulou, Ernestina Menasalvas
  • Towards a Methodology for Data mining Project
    Development The Importance of Abstraction.
    Menasalvas, Millán, Gonzalez-Aranda, Segovia
  • Bettina Berendt, Andreas Hotho, Dunja Mladenic,
    Maarten van Someren, Myra Spiliopoulou, Gerd
    Stumme Web Mining From Web to Semantic Web,
    First European Web Mining Forum, EMWF 2003,
    Cavtat-Dubrovnik, Croatia, September 22, 2003,
    Revised Selected and Invited Papers Springer 2004
  • Myra Spiliopoulou, Carsten Pohle Modelling and
    Incorporating Background Knowledge in the Web
    Mining Process. Pattern Detection and Discovery
    2002 154-169
  • www.crisp-dm.org
  • www.spss.com/clementine/cats.htm
  • www.sas.com/technologies/analytics/datamining/mine
    r/semma.html
  • www.crmmethodology.com
  • www.emetrics.org/articles/whitepaper.html

43
THANKS
Write a Comment
User Comments (0)
About PowerShow.com