Advanced Pattern Mining - Data Mining: Concepts and Techniques - page 281

Based on the abstraction levels involved in a pattern: Patterns or association rules
may have items or concepts residing at high, low, or multiple abstraction levels. For
example, suppose that a set of association rules mined includes the following rules
where X is a variable representing a customer:

buys . X , “ computer ” /) buys . X , “ printer ” / (7.1)

buys . X , “ laptop computer ” /) buys . X , “ color laser printer ” / (7.2)

In Rules (7.1) and (7.2), the items bought are referenced at different abstraction levels
(e.g., “ computer ” is a higher-level abstraction of “ laptop computer ,” and “ color laser
printer ” is a lower-level abstraction of “ printer ”). We refer to the rule set mined as
consisting of multilevel association rules . If, instead, the rules within a given set do
not reference items or attributes at different abstraction levels, then the set contains
single-level association rules .
Based on the number of dimensions involved in the rule or pattern: If the items
or attributes in an association rule or pattern reference only one dimension, it is a
single-dimensional association rule/pattern . For example, Rules (7.1) and (7.2) are
single-dimensional association rules because they each refer to only one dimension,
buys . 1
If a rule/pattern references two or more dimensions, such as age, income , and buys ,
then it is a multidimensional association rule/pattern . The following is an example
of a multidimensional rule:

age . X , “20 ::: 29” /^ income . X , “52 K ::: 58 K ” /) buys . X , “ iPad ” / . (7.3)

Based on the types of values handled in the rule or pattern: If a rule involves associ-
ations between the presence or absence of items, it is a Boolean association rule . For
example, Rules (7.1) and (7.2) are Boolean association rules obtained from market
basket analysis.
If a rule describes associations between quantitative items or attributes, then it
is a quantitative association rule . In these rules, quantitative values for items or
attributes are partitioned into intervals. Rule (7.3) can also be considered a quan-
titative association rule where the quantitative attributes age and income have been
discretized.
Based on the constraints or criteria used to mine selective patterns : The patterns
or rules to be discovered can be constraint-based (i.e., satisfying a set of user-
defined constraints), approximate , compressed , near-match (i.e., those that tally
the support count of the near or almost matching itemsets), top- k (i.e., the k most
frequent itemsets for a user-specified value, k ), redundancy-aware top- k (i.e., the
top- k patterns with similar or redundant patterns excluded), and so on.

1 Following the terminology used in multidimensional databases, we refer to each distinct predicate in a
rule as a dimension.