Advanced Pattern Mining - Data Mining: Concepts and Techniques - page 284

Table 7.1 Task-Relevant Data, D

TID ItemsPurchased
T100

Apple 17 00 MacBook Pro Notebook, HP Photosmart Pro b9180

T200 Microsoft Office Professional 2010, Microsoft Wireless Optical Mouse 5000

T300 Logitech VX Nano Cordless Laser Mouse, Fellowes GEL Wrist Rest

T400 Dell Studio XPS 16 Notebook, Canon PowerShot SD1400

T500 Lenovo ThinkPad X200 Tablet PC, Symantec Norton Antivirus 2010

. . . . . .

all

Computer Software Printer and Camera Computer Accessory

Digital
Camera

Laptop Desktop Office Antivirus Printer Wrist Pad Mouse

Dell Microsoft HP Canon Fellowes LogiTech

IBM

Figure 7.2 Concept hierarchy for AllElectronics computer items.

Concept hierarchies for nominal attributes are often implicit within the database
schema, in which case they may be automatically generated using methods such as those
described in Chapter 3. For our example, the concept hierarchy of Figure 7.2 was gene-
rated from data on product specifications. Concept hierarchies for numeric attributes
can be generated using discretization techniques, many of which were introduced in
Chapter 3. Alternatively, concept hierarchies may be specified by users familiar with the
data such as store managers in the case of our example.
The items in Table 7.1 are at the lowest level of Figure 7.2's concept hierarchy. It is
difficult to find interesting purchase patterns in such raw or primitive-level data. For
instance, if “ Dell Studio XPS 16 Notebook ” or “ Logitech VX Nano Cordless Laser Mouse ”
occurs in a very small fraction of the transactions, then it can be difficult to find strong
associations involving these specific items. Few people may buy these items together,
making it unlikely that the itemset will satisfy minimum support. However, we would
expect that it is easier to find strong associations between generalized abstractions of
these items, such as between “ Dell Notebook ” and “ Cordless Mouse. ”

Association rules generated from mining data at multiple abstraction levels are
called multiple-level or multilevel association rules . Multilevel association rules can be