Applications of Data Mining to Electronic Commerce by Ron Kohavi, Foster Provost (auth.), Ron Kohavi, Foster

By Ron Kohavi, Foster Provost (auth.), Ron Kohavi, Foster Provost (eds.)

Applications of information Mining to digital Commerce brings jointly in a single position vital contributions and up to date examine leads to this fast paced region.
Applications of information Mining to digital Commerceserves as a good reference, offering perception into essentially the most hard learn matters within the field.

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Extra resources for Applications of Data Mining to Electronic Commerce

Example text

Then we applied the filtering operator (operator 2 in figure 8) that rejects all the rules with household demographics-related infonnation in their heads.

For each rule r in the input rule set R (line 3) we compute its aggregated rule r' using the procedure AGGR_ATTRS (lines 5-6). The procedure AGGR_ATTRS (lines 11-15) performs the aggregation of a set of attributes. Using the mapping cute, each element of an attribute set is aggregated in constant time. Moreover, since the attribute set AttrSet is implemented as a hash table, ang insertion of an aggregated attribute into the resulting set A' (line 13, inside the loop) also takes constant time. Therefore, the total running time of the procedure AGGRJtTTRS is linear in the size of the attribute set.

We found, for example, that tobacco purchasers also buy more and stronger deodorizers and a different set of wines than non-tobacco purchasers. 7. The 16 SmartPad product categories are a superset of the 99 Safeway product classes, minus those classes like petrol and tobacco that are not available via SmartPad. 8. It is also possible that SmartPad participants in the new store were inherently more inclined to accept recommendations than those from the original store. We note that the demographics of the two stores are similar.

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