Hybrid association rules mining

There are two interesting applications of association rules mining: one is multilevel and multidimensional association rules mining, while the other is constraint-based mining.

Mining multilevel and multidimensional association rules

For a given transactional dataset, if there is a conceptual hierarchy that exists from some dimensions of the dataset, then we can apply multilevel association rules mining to this dataset. Any association rules mining algorithm applicable to the transaction dataset can be used for this task. The following table shows an example from the Amazon store:

TID

Item purchased

1

Dell Venue 7 16 GB Tablet, HP Pavilion 17-e140us 17.3-Inch Laptop...

2

Samsung Galaxy Tab 3 Lite, Razer Edge Pro 256GB Tablet…

2

Acer C720P-2666 Chromebook, Logitech Wireless Combo MK270 with Keyboard and Mouse…

2

Toshiba CB35-A3120 13.3-Inch Chromebook, Samsung Galaxy Tab 3 (7-Inch, White)…

Have a look at the following flowchart that explains multilevel pattern mining:

Mining multilevel and multidimensional association rules

Based on the conceptual hierarchy, lower-level concepts can be projected to higher-level concepts, and the new dataset with higher-level concepts can replace the original lower-level concepts.

The support counts are calculated at each conceptual level. Many A-Priori-like algorithms are designed with slightly different treatment to support count; here is a possible list of treatments available:

  • A uniform minimum support threshold is used across all the levels
  • Reduced minimum support threshold is used for lower levels
  • Group-based minimum support threshold

Note

Sometimes the A-Priori property is not always held here. There are some exceptions.

Multilevel association rules are mined from multiple levels of the conceptual hierarchy.

Constraint-based frequent pattern mining

Constraint-based frequent pattern mining is a heuristic method with some user-specified constraints to prune the search space.

The ordinary constraints are, but not limited to, the following:

  • Knowledge-type constraint (specifies what we are going to mine)
  • Data constraint (limits to the original dataset)
  • Dimension-level constraints
  • Interestingness constraints
  • Rule constraints
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