Data Mining Fundamentals

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Part I

• Data Mining Fundamentals

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Data Mining A First View

• Chapter 1

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1.1 Data Mining A Definition
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Data Mining

• The process of employing one or more computer
  learning techniques to automatically analyze and
  extract knowledge from data.

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Induction-based Learning

• The process of forming general concept
  definitions by observing specific examples of
  concepts to be learned.

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Knowledge Discovery in Databases (KDD)

• The application of the scientific method to data
  mining. Data mining is one step of the KDD
  process.

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1.2 What Can Computers Learn?
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Four Levels of Learning

• Facts
• Concepts
• Procedures
• Principles

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Concepts

• Computers are good at learning concepts. Concepts
  are the output of a data mining session.

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Three Concept Views

• Classical View
• Probabilistic View
• Exemplar View

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Supervised Learning

• Build a learner model using data instances of
  known origin.
• Use the model to determine the outcome new
  instances of unknown origin.

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• Supervised Learning
• A Decision Tree Example

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Decision Tree

• A tree structure where non-terminal nodes
  represent tests on one or more attributes and
  terminal nodes reflect decision outcomes.

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Production Rules

• IF Swollen Glands Yes
• THEN Diagnosis Strep Throat
• IF Swollen Glands No Fever Yes
• THEN Diagnosis Cold
• IF Swollen Glands No Fever No
• THEN Diagnosis Allergy

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Unsupervised Clustering

• A data mining method that builds models from data
  without predefined classes.

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1.3 Is Data Mining Appropriate for My Problem?
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Data Mining or Data Query?

• Shallow Knowledge
• Multidimensional Knowledge
• Hidden Knowledge
• Deep Knowledge

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Data Mining vs. Data Query An Example

• Use data query if you already almost know what
  you are looking for.
• Use data mining to find regularities in data
  that are not obvious.

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1.4 Expert Systems or Data Mining?
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Expert System

• A computer program that emulates the
  problem-solving skills of one or more human
  experts.

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Knowledge Engineer

• A person trained to interact with an expert in
  order to capture their knowledge.

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1.5 A Simple Data Mining Process Model
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Assembling the Data

• The Data Warehouse
• Relational Databases and Flat Files

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• Mining the Data

Interpreting the Results
Result Application
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1.7 Data Mining Applications