A generalized suffix tree and its (un)expected asymptotic behaviors
SIAM Journal on Computing
Patricia tries again revisited
Journal of the ACM (JACM)
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
A new and versatile method for association generation
Information Systems
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Beyond Independence: Probabilistic Models for Query Approximation on Binary Transaction Data
IEEE Transactions on Knowledge and Data Engineering
A Support-Ordered Trie for Fast Frequent Itemset Discovery
IEEE Transactions on Knowledge and Data Engineering
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As large-scale databases become commonplace, there has been significant interest in mining them for commercial purposes. One of the basic tasks that underlie many of these mining operations is querying of transaction sets for frequencies of specified attribute values. The size of these databases makes it important to develop summary structures capable of high compression ratios as well as supporting fast frequency queries. The nature of the problem and its differences with respect to traditional text compression allows very high compression ratios.In this paper, we propose a binary trie-based summary structure for representing transaction sets. We demonstrate that this trie structure, when augmented with an appropriate set of horizontal pointers, can support frequency queries several orders of magnitude faster than raw transaction data. We improve the memory characteristics of our scheme by compressing the trie into a Patricia trie and demonstrate that this does not have a significant adverse effect on frequency query time.We further reduce the size of this trie by selectively pruning branches to compute a 驴dominant驴 trie that is capable of approximate frequency querying. The complement trie called the 驴deviant驴 trie is also useful in many data mining applications. Recompressing the 驴dominant驴 trie into a Patricia trie results in further compression of the trie. Finally, we demonstrate that our binary compressed trie structure has better memory (compression) characteristics compared to related schemes. We support our claims with experimental results on datasets from the IBM synthetic association data generator.