  Itemset mining has been an active area of research due to its successful
application in various data mining scenarios including finding association
rules. Though most of the past work has been on finding frequent itemsets,
infrequent itemset mining has demonstrated its utility in web mining,
bioinformatics and other fields. In this paper, we propose a new algorithm
based on the pattern-growth paradigm to find minimally infrequent itemsets. A
minimally infrequent itemset has no subset which is also infrequent. We also
introduce the novel concept of residual trees. We further utilize the residual
trees to mine multiple level minimum support itemsets where different
thresholds are used for finding frequent itemsets for different lengths of the
itemset. Finally, we analyze the behavior of our algorithm with respect to
different parameters and show through experiments that it outperforms the
competing ones.
