Limiting Privacy Breaches in Privacy Preserving Data Mining
Alexandre Evfimievski, Johannes Gehrke, et al.
SIGMOD/PODS/ 2003
We introduce the problem of mining generalized association rules. Given a large database of transactions, where each transaction consists of a set of items, and a taxonomy (is-a hierarchy) on the items, we find associations between items at any level of the taxonomy. For example, given a taxonomy that says that jackets is-a outerwear is-a clothes, we may infer a rule that "people who buy outerwear tend to buy shoes". This rule may hold even if rules that "people who buy jackets tend to buy shoes", and "people who buy clothes tend to buy shoes" do not hold. An obvious solution to the problem is to add all ancestors of each item in a transaction to the transaction, and then run any of the algorithms for mining association rules on these "extended transactions". However, this "Basic" algorithm is not very fast; we present two algorithms, Cumulate and EstMerge, which run 2 to 5 times faster than Basic (and more than 100 times faster on one real-life dataset). Finally, we present a new interest-measure for rules which uses the information in the taxonomy. Given a user-specified "minimum-interest-lever", this measure prunes a large number of redundant rules; 40-60% of all the rules were pruned on two real-life datasets.
Alexandre Evfimievski, Johannes Gehrke, et al.
SIGMOD/PODS/ 2003
Rakesh Agrawal, Linda G. Demichiel, et al.
ACM SIGPLAN Notices
Rakesh Agrawal, Ramakrishnan Srikant
SIGMOD 2000
Ramakrishnan Srikant, Rakesh Agrawal
SIGMOD Record (ACM Special Interest Group on Management of Data)