G4G0-2/AI & Data Mining/Week 1/Lecture 1 - Introduction to Data Mining.md

Assessment

T1

• Exam (50%)

T2

• Coursework (50%)

Resources

Data Mining: Practical Machine Learning Tools and Techniques (Witten, Frank, Hall & Pal) 4th Edition 2016

Scientific Calculator

Data Vs Information

• Too much data
• Valuable resource
• Raw data less important, need to develop techniques to extract information
  • Data: recorded facts
  • Information: patterns underlying data

Philosophy

Cow Culling

• Cows described by 700 features about certain variables
• Problem is the selection of cows of which to cull
• Data is historical records, and farmer decisions
• Machine Learning used to ascertain which factors taken into account by farmers, rather than automating the decision making process.

Definition of Data Mining

• The extraction of:
  • Implicit,
  • Previously unknown,
  • Potentially useful data
• Programs that detect patterns and regularities are needed
• Strong patterns => good predictions
  • Issues:
    • Most patterns not interesting
    • Patterns may be inexact
    • Data may be garbled or missing

Machine Learning Techniques

• Algorithms for acquiring structural descriptions from examples
• Structural descriptions represent patterns, explicitly.
  • Predict outcome in new situation
  • Understand and explain how prediction derived.
• Methods originate from AI, statistics and research on databases.

Can Machines Learn?

• By definition, sort of. The ability to obtain knowledge by study, experience or being taught, is very difficult to measure.
• Does learning imply intention?

Terminology

• Concept - Thing to be learned
• Example / Instance - Individual, independent examples of a concept
• Attributes / Features - Measuring aspects of an example / instance
• Concept description (pattern, model, hypothesis) - Output for data mining algorithms.

Famous Small Datasets

• Will be used in module
• Unrealistically simple

Weather Dataset - Nominal

Concept: conditions which are suitable for a game. Reference: Quinlan, J.R. (1986) Induction of decision trees. Machine Learning, 1(1), 81-106.

Attributes

3*3*2*2 = 36 possible combinations of values. Outlook

• sunny, overcast, rainy Temperature
• hot, mild, cool Humidity
• high, normal Windy
• yes, no Play
• Class
• yes, no

Dataset

Rules ordered, higher = higher priority

Weather Dataset - Mixed

Contact Lenses Dataset

Describes conditions under which an optician might want to prescribe soft, hard or no contact lenses. Grossly over-simplified. Reference: Cendrowska, J. (1987). Prism: an algorithm for inducing module rules. Journal of Man-Machine Studies, 27(4), 349–370.

Attributes

3*2*2*2 = 24 possibilities Dataset is exhaustive, which is unusual.

Age

• young, pre-presbyopic, presbyopic Spectacle Prescription
• myope (short), hypermetrope (long) Astigmatism
• yes, no Tear Production Rate
• reduced, normal Recommended Lenses
• class
• hard, soft, none

Dataset

Iris Dataset

Used in many statistical experiments Contains numeric attributes of 3 different types of iris. Created in 1936 by Sir Ronald Fisher

Dataset

Styles of Learning

• Classification Learning: Predicting a nominal class
• Numeric Prediction (Regression): Predicting a numeric quantity
• Clustering: Grouping similar examples into clusters
• Association Learning: Detecting associations between attributes

Classification Learning

• Nominal
• Supervised
  • Provided with actual value of the class
• Measure success on fresh data for which class labels are known (test data)

Numeric Prediction (Regression)

• Numeric
• Supervised
• Test Data

Example uses a linear regression function to provide an estimated performance value based on attributes.

Clustering

• Finding similar groups
• Unsupervised
  • Class of example is unknown
• Success measured subjectively

Association Learning

• Applied if no class specified, and any kind of structure is interesting
• Difference to Classification Learning:
  • Predicts any attribute's value, not just class.
  • More than one attribute's value at a time
  • Far more association rules than classification rules.

Classification Vs Association Rules

Classification Rule:

• Predicts value of a given attribute (class of example)
• If outlook = sunny and humidity = high, then play = no

Association Rule:

• Predicts value of arbitrary attribute / combination

If humidity = normal and windy = false, play = yes
If outlook = sunny and play = no, humidity = high
If windy = false and play = no, then outlook = sunny and humidity = high

Data Mining and Ethics

• Ethical Issues arise in practical applications
• Data mining often used to discriminate
• Ethical situation depends on application
• Attributes may contain problematic information
• Does ownership of data bestow right to use it in other ways than those purported when it was originally collected?
• Who is permitted to access the data?
• For what purpose was the data collected?
• What conclusions can sensibly be drawn?
• Caveats must be attached to results
• Purely statistical arguments never sufficient