Advances in frequent itemset mining implementations

Goethals, Bart; Zaki, Mohammed J.

doi:10.1145/1007730.1007744

Cited by 275 publications

(215 citation statements)

References 20 publications

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“…We use benchmark datasets arising arising in frequent pattern mining [22], where each "string" is a subset of a large alphabet (up to tens of thousands). In some frequent pattern mining algorithms such as [23], these strings need to be traversed in sorted order, which takes a slow O(nσ) time in all Bonsai variants because they do not support the next-sibling operation.…”

Section: Experimental Analysismentioning

confidence: 99%

Improved Practical Compact Dynamic Tries

Poyias

Raman

2015

String Processing and Information Retrieval

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Abstract. We consider the problem of implementing a dynamic trie with an emphasis on good practical performance. For a trie with n nodes with an alphabet of size σ, the information-theoretic lower bound is n log σ + O(n) bits. The Bonsai data structure [1] supports trie operations in O(1) expected time (based on assumptions about the behaviour of hash functions). While its practical speed performance is excellent, its space usage of (1 + )n(log σ + O(log log n)) bits, where is any constant > 0, is not asymptotically optimal. We propose an alternative, m-Bonsai, that uses (1 + )n(log σ + O(1)) bits in expectation, and supports operations in O(1) expected time (again based on assumptions about the behaviour of hash functions). We give a heuristic implementation of mBonsai which uses considerably less memory and is slightly faster than the original Bonsai.

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Section: Experimental Analysismentioning

confidence: 99%

Improved Practical Compact Dynamic Tries

Poyias

Raman

2015

String Processing and Information Retrieval

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“…This is a generalization of the problem of mining frequent item-sets that has been a topic of intensive research (see e.g. Agrawal et al, 1996;Goethals & Zaki, 2004;Han et al, 2000). When mining frequent item-sets, there is a single relation whose arguments give an exhaustive list of items and whose tuples describe subsets of items.…”

Section: Introductionmentioning

confidence: 99%

Mining closed patterns in relational, graph and network data

Garriga

Khardon

Raedt

2012

Ann Math Artif Intell

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Recent theoretical insights have led to the introduction of efficient algorithms for mining closed item-sets. This paper investigates potential generalizations of this paradigm to mine closed patterns in relational, graph and network databases. Several semantics and associated definitions for closed patterns in relational data have been introduced in previous work, but the differences among these and the implications of the choice of semantics was not clear. The paper investigates these implications in the context of generalizing the LCM algorithm, an algorithm for enumerating closed item-sets. LCM is attractive since its run time is linear in the number of closed patterns and since it does not need to store the patterns output in order to avoid duplicates, further reducing memory signature and run time. Our investigation shows that the choice of semantics has a dramatic effect on the properties of closed patterns and as a result, in some settings a generalization of the LCM algorithm is not possible. On the other hand, we provide a full generalization of LCM for the semantic setting that has been previously used by the Claudien system.

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“…Goethals and Zaki (2004) compare the currently fastest algorithms. Among these algorithms are the implementations of the Apriori and Eclat algorithms by Borgelt (2003) interfaced in the arules environment.…”

Section: Introductionmentioning

confidence: 99%

arules- A Computational Environment for Mining Association Rules and Frequent Item Sets

Hahsler¹,

Grün²,

Hornik³

2005

J. Stat. Soft.

436

337

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Mining frequent itemsets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases. The R package arules presented in this paper provides a basic infrastructure for creating and manipulating input data sets and for analyzing the resulting itemsets and rules. The package also includes interfaces to two fast mining algorithms, the popular C implementations of Apriori and Eclat by Christian Borgelt. These algorithms can be used to mine frequent itemsets, maximal frequent itemsets, closed frequent itemsets and association rules.

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Advances in frequent itemset mining implementations

Cited by 275 publications

References 20 publications

Improved Practical Compact Dynamic Tries

Improved Practical Compact Dynamic Tries

Mining closed patterns in relational, graph and network data

arules- A Computational Environment for Mining Association Rules and Frequent Item Sets

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