Part I Data Mining Fundamentals Chapter 1 Data Mining: A First View

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Part IData Mining Fundamentals Chapter 1Data
Mining A First View
Jason C. H. Chen, Ph.D. Professor of MIS School
of Business Administration Gonzaga
University Spokane, WA 99223 chen_at_jepson.gonzaga.e
du
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1.1 Data Mining A Definition
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1.1 Data Mining A Definition

• 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.

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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Data Mining Examples

• A telephone company used a data mining tool to
  analyze their customers data warehouse. The
  data mining tool found about 10,000 supposedly
  residential customers that were expending over
  1,000 monthly in phone bills.
• After further study, the phone company discovered
  that they were really small business owners
  trying to avoid paying business rates

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Other Data Mining Examples

• 65 of customers who did not use the credit card
  in the last six months are 88 likely to cancel
  their accounts.
• If age lt 30 and income lt 25,000 and credit
  rating lt 3 and credit amount gt 25,000 then the
  minimum loan term is 10 years.
• 82 of customers who bought a new TV 27" or
  larger are 90 likely to buy an entertainment
  center within the next 4 weeks.

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1.2 What Can Computers Learn?
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Four Levels of Learning

• Fact
• a simple statement of truth
• Concept
• a set of objects, symbols, or events grouped
  together because they share certain
  characteristics
• Principle
• is a step-by-step course of action to achieve a
  goal. We use procedures in our everyday
  functioning as well as in the solution of
  difficult problems
• Procedure
• represents the highest level of learning.
  Principles are general truths or laws that are
  basic to other truths.

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Source Merril and Tennyson, 1977, p.5 of the text
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Concepts

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

Three Concept Views

• Classical View
• Probabilistic View
• Exemplar View

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Three Concept Views

• Classical View
• Attests that all concepts have definite defining
  properties.
• Probabilistic View
• Concepts are represented by properties that are
  probable of concept members.
• Exemplar View
• States that a given instance is determined to be
  an example of a particular concept if the
  instance is similar enough to a set of one or
  more known examples of the concepts

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Figure - A hierarchy of data mining strategies
No output attributes
Categorical/discrete (current behavior)
Numeric
Future outcome (categorical/numeric)
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Supervised Learning
Supervised learning is the process of building
classification models using data instances of
known origin.

• Two purposes
• 1. Build a learner (classification) model using
  data instances of known origin.
• is an induction process
• 2. Use the model to determine the outcome new
  instances of unknown origin.
• is a deduction process

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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.

Table 1.1 Hypothetical Training Data for
Disease Diagnosis
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Figure 1.1 A decision tree for the data in
Table 1.1
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Table 1.1 Hypothetical Training Data for
Disease Diagnosis
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Production Rules
We can translate any decision tree into a set of
production rules. They are rules of the form IF
ltantecedent conditionsgt THEN ltconsequent
conditionsgt

• 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 (see Table 1.3).
• Data instances are grouped together based on a
  similarity scheme defined by the clustering
  system.
• With the help of one or several evaluation
  techniques, it is up to us to decide the meaning
  of the formed clusters.

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Table 1.3 Acme Investors Incorporated
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Possible Questions
Questions for supervised learning

• 1. Can I develop a general profile of an online
  investor? If so, what characteristics distinguish
  online investors from investors that use a
  broker?
• 2. Can I determine if a new customer who does not
  initially open a margin account is likely to do
  so in the future?
• 3. Can I build a model able to accurately predict
  the average number of trades per month for a new
  investor?
• 4. What characteristics differentiate female and
  male investors?

Questions for unsupervised learning

• 1. What attribute similarities group customers of
  Acme Investors together?
• 2. What differences in attribute values segment
  the customer database?

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

• Shallow Knowledge
• is factual tools used DBMS/SQL
• Multidimensional Knowledge
• Is factual tools used OLAP
• Hidden Knowledge
• Represents patterns or regularities in data that
  cannot be easily found, tools used data mining
• Deep Knowledge
• Knowledge stored in a database that can only be
  found if we are given some direction.

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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 and Knowledge Engineer

• An expert system is a computer program that
  emulates the problem-solving skills of one or
  more human experts.
• A knowledge engineer is 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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Figure 1.3 - A simples data mining process model
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Characteristics of Data Warehouse

• Data Warehouse
• Definitions a subject-oriented, integrated,
  time-variant, non-updatable collection of data
  used in support of management decision-making
  processes
• Subject-oriented e.g. customers, patients,
  students, products
• Integrated Consistent naming conventions,
  formats, encoding structures from multiple data
  sources
• Time-variant Can study trends and changes
• Nonupdatable Read-only, periodically refreshed
• Data Mart
• A data warehouse that is limited in scope

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A four-step process for performing a data mining
session

• 1. Assembling the data
• Operational database (relational databases and
  flat files) vs. data warehouse
• 2. Mining the Data (Giving the data to a mining
  tool)
• Instances for building the model or testing the
  model
• 3. Interpreting the results
• 4. Result application

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1.7 Data Mining Applications (p.24)

• Fraud Detection
• Health care
• Business and finance
• Scientific applications
• Sports and gaming

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Customer Intrinsic Value
B
A
C