AI Week 15 Machine Learning: Data Mining : Association Rule Mining, Associative Classification, Applications

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AI Week 15Machine LearningData Mining
Association Rule Mining, Associative
Classification,Applications

• Lee McCluskey, room 3/10
• Email lee_at_hud.ac.uk
• http//scom.hud.ac.uk/scomtlm/cha2555/

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Last Week
Data Mining -- as inducing rule classifiers from
classified training examples.
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Association Rule Mining(ARM)

• This is an unsupervised learning activity -
  briefly, looking for strong associations between
  features in data.
• Definitions A transactional database is a set of
  transactions eg the details of individual
  sales.
• A transaction can be though of as an item-set
  where each item is an attribute-value
• height6, temp 20. weather warm
• As a special case we could have nominal item sets
• bread, cheese, milk

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Association Rule Mining(ARM) Important
Definitions

• An association rule is an expression
• X gt Y
• where X, Y are item-sets, and
• The support of an association rule is defined as
  the proportion of transactions in the database
  that contain
• X U Y.
• The confidence of an association rule is defined
  as the probability that a transaction contains Y
  given that it contains X, that is
• no of transactions containing (X U Y) / no of
  transactions containing X

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Aims of ARM

• Given a transactional database D, the association
  rule problem is to find all rules that have
  supports and confidences greater than certain
  user-specified thresholds, denoted by minimum
  support (MinSupp) and minimum confidence
  (MinConf), respectively.
• The aim is the discovery of the most significant
  associations between the items in a transactional
  data set. This process involves primarily the
  discovery of so called frequent item-sets, i.e.
  item-sets that occurred in the transactional data
  set above MinSupp and MinConf.

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Example

• A trader deals in the following currencies in a
  series of 8 transactions
• 1 Sterling Yen Dollar Euro
• 2 Dollar Euro Rand Sterling Ruble
• 3 Pesos Euro Ruble Rupee Yen
• 4 Rupee Sterling Ruble Euro Dollar
• 5 Sterling Dinars Rand Yen
• 6 Pesos Kroner Sterling Dollar
• 7 Ruble Rupee Kroner Sterling Pesos
• 8 Dollar Euro Sterling
• What is the SUPPORT and CONFIDENCE of the
  following rules?
• Ruble ? Rupee
• Sterling, Euro ? Ruble
• Sterling, Euro ? Ruble, Pesos
• Find an association rule from the set of
  transactions that has
• - at least 2 items in its antecedents,
• - better support and better confidence than both
  rules above.

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Example
Sterling Yen Dollar Euro
Sterling Yen Dollar Euro
Sterling Yen Dollar Euro
Pesos Kroner Sterling Dollar
Sterling Dinars Rand Yen
Dollar Euro Sterling
X
Pesos Euro Ruble Rupee Yen
Ruble Rupee Kroner Sterling Pesos
Dollar Euro Rand Sterling Ruble
X u Y
R
X gt Y Ruble gt Rupee
Rupee Sterling Ruble Euro Dollar
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Associative Classification

• If we fuse ARM and classification rule mining we
  get Associative Classification use the
  association technique, but learning about
  particular items or item sets.
• Associative Classification is a branch in data
  mining that combines classification and
  association rule mining. In other words, it
  utlises association rule discovery methods in
  classification data sets.
• Typically
• Find Association Rules using ARM
• Sift out the Class Association Rules ones
  that have the class of interest on their Right
  Hand Sides

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Validation in Rule Discovery

• Multi-stage Data Mining pipelines are fraught
  with various kinds of errors / bias
• the integrity of the data at each stage of the DM
  process and the reliability of the results are
  particularly important.
• DM usually uses cross validation, where the
  data is split into a training set and a testing
  set, and the results of the data miner applied to
  the training set is compared to the training set.
  Not really applicable to rule discovery.
• Key idea Look for trends/associations in the
  data that are output from the process and that
  represent known associations in the application
  domain.

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DM Application 1 Discovering trends from patient
data in the area of Diabetic Retinopathy

• Diabetic Retinopathy Basically damage to the
  eyes caused by Diabetes, sometimes leading to
  blindness
• HUGE problem as diabetes on the increase. If you
  are a long term diabetic then your are very
  likely suffer some retina damage
• Clinics keep large amounts of data on patients
  who are treated in various ways, over long
  periods of time.

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Diabetic Retinopathy Application

• Data of 20,000 patients over 18 years
• Much data cleaning and inference precedes mining
  replacing missing values, noise, anomalies etc
• Focus in one a smaller number of patients with a
  yearly screening (- timestamp) over a period of
  4 years
• Attribute Examples (there are several hundred)
• Age_at_Exam ,
• Present_Treatment,
• calculated_age_at_diagnosis,
• Retinopathy_in_R_Eye (RE_RET),
• Retinopathy_in_L_Eye (RE_RET),
• calculated_diabetes_type,
• calculated_diabetes_duration

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Trend Mining

• Item-sets that have an increasing support over a
  series of time-stamped instances (events) are
  called emerging patterns
• The changing support for sets of items during
  each event can indicate trends in the data. For
  example, the presence of a particular treatment
  over a period of time may lead to the alleviation
  of a symptom.

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Diabetic Retinopathy Application

• Aim - to find trends in the data e.g. (ficticous
  example)
• calculated_diabetes_duration gt Y
• Age_at_Exam in 60,70
• Present_Treatment drugX
• calculated_age_at_diagnosis in 50,60 gt
• Retinopathy_in_R_Eye (RE_RET) low
• Retinopathy_in_L_Eye (RE_RET) low
• Increasing trend ..
• people who have had diabetes for a certain
  length of time, whose age is in there 60s, who
  were diagnosed in their 50s, who have been
  taking treatmentX, often have low DR levels
• Increasing trend adds support for the
  association.

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DM Application 1 Road Traffic Control
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Example in Road Traffic Control
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Example in Road Traffic Control

• Data ..
• Numeric Data Record from individual CARS
• (date, time, position, actual speed, expected
  speed)
• Textual Data of INCIDENTS
• (date, time start, time cleared, position,
  severity, road type, area, incident category,
  cause, road-effect, traffic-effect, reporter ..)
• Data Sources ..
• ANPR, Mobile Phones, Road (Vehicle) Sensors,
  Environment Sensors

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Applications in Road Traffic Control

• associations between variations in speeds with
  near-future incidents
• effect of a particular type of incident (eg
  roadworks) on average speeds on nearby trunk
  roads
• looking for predictors in "heavy/slow traffic"
  incidents look for associations with speed
  variations or accidents on roads downstream from
  the incident position (hence causing the
  incident)
• looking for associations between speeds around a
  bypass and a later "heavy traffic" incident
  within the town bypassed

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Conclusions

• Data Mining is a powerful set of techniques to
  help discover hidden knowledge
• It can be supervised or unsupervised.
• Association Rule Mining
• Associative Classification
• are important classes of technique used in DM