Data mining and the knowledge discovery process

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Data mining and the knowledge discovery process

• Summer Course 2005
• H.H.L.M. Donkers

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Content

• Opening / acquaintance
• What is data mining
• Data mining methodology
• Course perspective
• Course contents

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Data - Information - Knowledge -

• Data symbols
• Information data that are processed to be
  useful provides answers to "who", "what",
  "where", and "when" questions
• Knowledge application of data and information
  answers "how" questions
• Understanding appreciation of "why"
• Wisdom evaluated understanding. (Russell
  Ackoff - http//www.outsights.com/systems/dikw/dik
  w.htm)

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Data - Information - Knowledge -

• http//www.outsights.com/systems/dikw/dikw.htm

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What is Data Mining Traditionally

• Data mining is the extraction of implicit,
  previously unknown, and potentially useful
  information from data.
• Witten Frank (2000). Data Mining.

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What is Data Mining Traditionally

• The application of specific algorithms for
  extracting patterns from data, it is a part of
  knowledge discovery from databases
• Fayyad (1997). From data mining to knowledge
  discovery in databases.

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What is Data Mining Traditionally

• Data mining is a process, not just a series of
  statistical analyses.
• SAS Institute (2003). Finding the solution to
  data mining.

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What is Data Mining Traditionally

• Computer Science
• (Semi-)automated application of algorithms for
  pattern discovery
• Algorithms developed in the field of Artificial
  Intelligence (machine learning)
• Part of the process of knowledge discovery

• Statistics
• Process of discovering patterns in data
• (Manual) application of a series of statistical
  techniques (among which machine learning)
• Incorporates
• Exploration
• Sampling
• Modeling
• Validation

Data mining Statistics Marketing
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What is Data Mining A Fusion

• An analytic process designed to explore data in
  search of consistent patterns and/or systematic
  relationships between variables, and then to
  validate the findings by applying the detected
  patterns to new subsets of data. The ultimate
  goal is prediction.
• Statsoft (2003). Data Mining Techniques.

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What is Data Mining A Fusion

• An information extraction activity whose goal is
  to discover hidden facts contained in databases.
  Using a combination of machine learning,
  statistical analysis, modeling techniques and
  database technology, data mining finds patterns
  and subtle relationships in data and infers rules
  that allow the prediction of future results.
• Rudjer Boskovic Institute (2001). DMS Tutorial.

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Data Mining In This Course

• We use the book of Witten Frank
• Computer science (machine learning) approach
• Emphasis on algorithms for pattern discovery and
  rule extraction
• What are the underlying models
• What are the properties of the algorithms
• When to use (for which tasks)
• How to apply and to tune
• How to interpret and assess the results

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Data Mining Process

• These algorithms are only part of a process that
  computer scientists call Knowledge Discovery and
  the statisticians call Data Mining
• The process starts with the recognition of a
  problem and ends with the control of a deployed
  solution
• The whole process needs to be supported for a
  successful application

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Methodologies for Data Mining

• As Data Mining is coming of age, several
  methodologies have been developed, each with
  their own perspective. We will discuss three of
  them
• Fayyad et al. (Computer science)
• E.g., WEKA
• SEMMA (SAS) (Statistics)
• SAS Enterprise Miner
• CRISP-DM (SPSS, OHRA, a.o.) (Business)
• SPSS Clementine

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Fayyads KDD Methodology
data
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SEMMA Methodology
Supported by SAS Enterprise Mining environment
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CRISP-DM Methodology

• Developed by data-mining companies (SPSS, NCR,
  OHRA, ChryslerDaimler), funded by the European
  Commission
• Tool-independent / industry-independent
• Hierarchical process model
• 1 Generic phases 2 Generic tasks
• 3 Specific tasks 4 Task instances
• Supported by SPSS Clementine environment

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CRISP-DM Methodology
TASKS Business objective Assess situation Data
mining goals Project plan
Business understanding
Data understanding
Data Preparation
Deployment
Modeling
Evaluation
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CRISP-DM Methodology
TASKS Collect data Describe data Explore
data Verify data quality
Business understanding
Data understanding
Data Preparation
Deployment
Modeling
Evaluation
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CRISP-DM Methodology
TASKS Select data Clean data Construct
data Integrate data Format data
Business understanding
Data understanding
Data Preparation
Deployment
Modeling
Evaluation
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CRISP-DM Methodology
TASKS Select modeling techniques Design the
test Build model Assess model
Business understanding
Data understanding
Data Preparation
Deployment
Modeling
Evaluation
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CRISP-DM Methodology
TASKS Evaluate results Review
process Determine next steps
Business understanding
Data understanding
Data Preparation
Deployment
Modeling
Evaluation
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CRISP-DM Methodology
TASKS Plan deployment Plan monitoring and
maintenance Final report Review project
Business understanding
Data understanding
Data Preparation
Deployment
Modeling
Evaluation
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A Comparison
Business understanding
Data understanding
Data Preparation
Deployment
Modeling
Evaluation
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A Small Poll (July 2002)
Source http//www.kdnuggets.com/polls/2002/method
ology.htm
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Course perspective and goal

• The perspective is from computer science
  (machine learning) Fayyads approach
• The emphasis is on techniques for the automated
  discovery of patterns in data and the automated
  extraction of rules (the model phase of SEMMA and
  CRISP)
• The goal is to get acquainted with these
  techniques, so you can use them in the
  methodology of your choice

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Course contents

• Data preparation (Wednesday)
• Selection, preprocessing, transformation
• Techniques, algorithms and models
• Decision trees (Monday)
• Instance based and Bayesian learning (Tuesday)
• Neural networks (Tuesday)
• Association rules (Thursday)
• Clustering (Thursday)
• Support Vector Machines (Friday)
• Evaluation of learned models (Wednesday)

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Course contents

• For each technique you learn
• For which tasks it is suitable
• Classification, rules, prediction,
• Restrictions on input data (numerical, symbolic,
  etc.)
• What algorithms are available
• What parameters should be tuned
• How to interpret the results
• How to evaluate the model