--partition Algorithm for Mining Association rules

Association rules are expressions such as a->b, and A and B are the two subkeys that intersect each other in the entire set.

The main purpose of mining association rules is to find meaningful correlation relationships in data. Shopping basket analysis is the analysis of customer purchase behavior to discover the relationship between different products.

Support degree, confidence level, promotion degree

Support Degree (a->b) =| ab|/| s|

Confidence level (A->B) =| ab|/| a|

This partition-based algorithm first divides the database logically into disjoint chunks, considers a block each time and generates all the frequency sets for it, then merges the resulting frequency set to generate all possible frequency sets, and finally calculates the support for these itemsets. Here the size of the block is chosen so that each block can be placed in the primary, and each stage is scanned only once. The correctness of the algorithm is guaranteed by the frequency set in at least one block of each possible frequency set. The algorithms discussed above can be highly parallel, and each chunk can be assigned to a processor to generate a frequency set. After each loop that generates the frequency set, the processor communicates between the processors to produce a global candidate K-Key set. Usually the communication process here is the main bottleneck of the algorithm execution time, on the other hand, the time that each independent processor generates the frequency set is also a bottleneck. Other methods also share a hash tree between multiple processors to generate the frequency set. More parallelization methods for generating frequency sets can be found in the literature [AS96] .

--partition Algorithm for Mining Association rules