出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Frequent itemset (FI) mining is an interesting data mining task. Directly mining the FIs from
data often requires lots of time and memory, and should be avoided in many cases. A more
preferred approach is to mine only the frequent closed itemsets (FCIs) first and then extract the
FIs for each FCI because the number of FCIs is usually much less than that of the FIs.
However, some algorithms require the generators for each FCI to extract the FIs, leading to an
extra cost. In this paper, based on the concepts of “kernel set” and “extendable set”, we
introduce the NUCLEAR algorithm which easily and quickly induces the FIs from the lattice of
FCIs without the need of the generators. Experimental results showed that NUCLEAR is
effective as compared to previous studies, especially, the time for extracting the FIs is usually
much smaller than that for mining the FCIs.
