An association rule has the form (X rightarrow Y) where X is a set of antecedent items and Y is the consequent item.
We consider each resulting association rule as a set, where each member of antecedent items in X and the item in Y are members of the set.
After the collection of the matrix, we carry out frequent pattern mining from the incidence matrix to discover association rules in the form of X (rightarrow) Y, where X is a set of antecedent items and Y is the consequent item.
Originally, it visualized many-to-one association rules (i.e. many items in the antecedent, but only one item in the consequent).
The matrix-based visualization technique presents the antecedent and consequent items on the X and Y axes.
The CDPM comprises 4 blocks: antecedent variables scale with 24 items, scale of medical burnout syndrome with 12 items, consequent variables scale with 16 items, and scale of positive personal resources with 16 items.
There is one rule which contains "Driver_sex = M", and two other items in the antecedent (LHS); the consequent (RHS) is "Accident_type = Fatal".
Finally, the indices (recall, specificity, precision,…) were extrapolated to the complete association rules database, including multi-element rules (with several items in the antecedent or in the consequent).
Our primary goal is to create a complete set of rules, containing multi-elements rules (including several items in the antecedent or in the consequent).
Retrieval interference may then shape processing complexity as a function of the match of the information at retrieval with the antecedent versus other recent or similar items in memory.
While recently examining one of the early classic works on breast cancer epidemiology Janet E. Lane-Claypon's 1926 report to the United Kingdom Ministry of Health [ 1] and its 1931 US counterpart [ 2], I was struck by one item in the list of commonplace "antecedent conditions": breast bruises, a not uncommon occurrence.
