Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Efficient mining of both positive and negative association rules
ACM Transactions on Information Systems (TOIS)
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
A New Algorithm for Finding Minimal Sample Uniques for Use in Statistical Disclosure Assessment
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Mining Infrequent Itemsets Based on Multiple Level Minimum Supports
ICICIC '07 Proceedings of the Second International Conference on Innovative Computing, Informatio and Control
A recursive search algorithm for statistical disclosure assessment
Data Mining and Knowledge Discovery
Mining Both Positive and Negative Association Rules from Frequent and Infrequent Itemsets
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Mining Interesting Infrequent and Frequent Itemsets Based on MLMS Model
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
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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.