Data mining with Association rules in law

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• Data mining with Association rules in law
• Andrew Stranieri and John Zeleznikow
• Donald Berman Laboratory for Information
  Technology and Law, La Trobe University,
• Australia
• http//www.cs.latrobe.edu.au/research/dbc/dbc.html
  
• stranier_at_cs.latrobe.edu.au

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Overview

• Data mining or Knowledge discovery from databases
  has not been appropriately exploited in law to
  date.
• Association rules are useful in that they
  suggest hypotheses for future research
• Association rules integrated into the generic
  actual argument model can assist in identifying
  the most plausible claim from given data items in
  a forward inference way or the likelihood of
  missing data values in a backward inference way

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• What is data mining ? What is knowledge discovery
  from databases KDD?

• Frawley et al 1991 define knowledge discovery
  in databases (KDD) as the 'non trivial extraction
  of nontrivial of implicit, previously unknown,
  and potentially useful information from data
• KDD encompasses a number of different technical
  approaches, such as clustering, data
  summarization, learning classification rules,
  finding dependency networks, analyzing changes,
  and detecting anomalies
• KDD has only recently emerged because we only
  recently have been gathering vast quantities of
  data
• Good introductory text by Thornton (1992),
  Weiss, S., and Indurkya, (1998).Weiss, S and
  Kulikowski, (1992)

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• Examples of KDD studies

• Mangasarian et al (1997) Breast Cancer
  diagnosis. A sample from breast lump mass is
  assessed by
• mammagrophy (not sensitive 68-79)
• data mining from FNA test results and visual
  inspection (65-98)
• surgery (100 but invasive, expensive)
• Basket analysis. People who buy nappies also buy
  beer
• NBA. National Basketball Association of America.
  Player pattern profile. Bhandary et al (1997)
• Credit card fraud detection
• Stranieri/Zeleznikow (1997) predict family law
  property outcomes
• Rissland and Friedman (1997) discovers a change
  in the concept of good faith in US Bankruptcy
  cases
• Pannu (1995) discovers a prototypical case from a
  library of cases
• Wilkins and Pillaipakkamnatt (1997) predicts the
  time a case takes to be heard
• Veliev et al (1999) association rules for
  economic analaysis

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• Overview of process of knowledge discovery in
  databases ?

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• Phase 4. Data mining

• Finding patterns in data or fitting models to
  data
• Categories of techniques
• Predictive (classification neural networks, rule
  induction, linear, multiple regression)
• Segmentation (clustering, k-means, k-median)
• Summarisation (associations, visualisation)
• Change detection/modelling

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• Association rules are a data mining technique

• An association rules tell us something about the
  association between two attributes
• Agrawal et al (1993) developed the first
  association rule algorithm, Apriori
• A famous (but unsubstantiated AR) from a
  hypothetical supermarket transaction database is
  if nappies then beer (80) Read this as nappies
  are bought implies beer are bought 80 of the
  time
• Association rules have only recently been
  applied to law with promising results
• Association rules can automatically discover
  rules that may prompt an analyst to think of
  hypothesis they would otherwise have considered

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• Confidence and support of an association rule

• 80 is the confidence of the rule if nappies
  then beer (80). This is calculated by n2/n1
  where
• n1 no of records where nappies are bought
• n2 no of records where nappies were bought and
  beer was also bought.
• if 1000 transactions for nappies, and of those,
  800 also had beer then confidence is 80.
• A rule may have a high confidence but not be
  interesting because it doesnt apply to many
  records in the database. i.e. no. of records
  where nappies were bought with beer / total
  records.
• Rules that may be interesting have a confidence
  level and support level above a user set
  threshold

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• Interesting rules Confidence and support of an
  association rule

• if 1000 transactions for nappies, and of those,
  800 also had beer then confidence is 80.
• A rule may have a high confidence but not be
  interesting because it doesnt apply to many
  records in the database. i.e. no. of records
  where nappies were bought with beer / total
  records.
• Rules that may be interesting have a confidence
  level and support level above a user set
  threshold

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Association rule screen shot with A-Miner from
Split Up data set

• In 73.4 of cases where the wife's needs are
  some to high then the husband's future needs are
  few to some.
• Prompts an analyst to posit plausible hypothesis
  e.g. it may be the case that the rule reflects
  the fact that more women remain custodial parents
  of the children following divorce than men do.
  The women that have some to high needs may do so
  because of their obligation to children.

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Association rules in law

• Association rules generators are typically
  packaged with very expensive data mining suites.
  We developed A-Miner (available from authors) for
  a PC platform.
• Typically, too many association rules are
  generated for feasible analysis. So, our current
  research involves exploring metrics of
  interesting to restrict numbers of rules that
  might be interesting
• In general, structured data is not collected in
  law as it is in other domains so very large
  databases are rare
• Our current research involves 380,000 records
  from a Legal Aid organization data base that
  contains data on client features.
• ArgumentDeveloper shell that can be used by
  judges to structure their reasoning in a way that
  will facilitate data collection and reasoning

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Generic/actual argument model for sentencing
armed robbery
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Association rules can be used for forward and
backward inferences in the generic/actual
argument model for sentencing armed robbery
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Forward inference confidence

• In the sentence actual argument database the
  following outcomes were noted for the inputs
  suggested

57 0.1 0 12 2 10 16 0 0 0
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Backward inference constructing the strongest
argument
If all the items you suggest AND
If extremely serious pattern of priors then
imprisonment If very serious pattern of priors
then imprisonment If serious pattern of priors
then imprisonment If not so serious pattern of
priors then imprisonment If no prior convictions
then imprisonment
90 2
75 7
68 17
78 17
2 3
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Conclusion

• Data mining or Knowledge discovery from databases
  has not been appropriately exploited in law to
  date.
• Association rules are useful in that they
  suggest hypotheses for future research
• Association rules integrated into the generic
  actual argument model can assist in identifying
  the most plausible claim from given data items in
  a forward inference way or the likelihood of
  missing data values in a backward inference way

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The process of constructing an argument