Min-Max Itemset Trees for Dense and Categorical Datasets

Lavergne, Jennifer; Benton, Ryan; Raghavan, Vijay V.

doi:10.1007/978-3-642-34624-8_6

Cited by 14 publications

(16 citation statements)

References 6 publications

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“…That is, they are designed to discover (and update) all association rules in a database (meeting the user-defined thresholds mentioned above) rather than allowing the user to perform targeted queries. Targeted queries are useful for applications where the user wants to discover association rules involving a subset of the items contained in a database, instead of all items [7,8]. To process targeted queries for association rule mining efficiently in the context of static or incremental databases, the Itemset Tree (IT) data structure was proposed [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…Targeted queries are useful for applications where the user wants to discover association rules involving a subset of the items contained in a database, instead of all items [7,8]. To process targeted queries for association rule mining efficiently in the context of static or incremental databases, the Itemset Tree (IT) data structure was proposed [7,8]. The IT is a tree structure, which can be incrementally updated and efficiently queried.…”

Section: Introductionmentioning

confidence: 99%

“…The IT is a tree structure, which can be incrementally updated and efficiently queried. The IT structure allows performing a vast array of important targeted queries such as (1) calculating the frequency of a given set of items, (2) discovering all valid association rules given a set of items as antecedent and (3) finding all frequent itemsets subsuming a set of items and their support [7,8]. The IT structure has various applications such as predicting missing items in shopping carts in real-time [9].…”

Section: Introductionmentioning

confidence: 99%

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MEIT: Memory Efficient Itemset Tree for Targeted Association Rule Mining

Fournier-Viger

Mwamikazi

Gueniche

et al. 2013

Advanced Data Mining and Applications

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The Itemset Tree is an efficient data structure for performing targeted queries for itemset mining and association rule mining. It is incrementally updatable by inserting new transactions and it provides efficient querying and updating algorithms. However, an important limitation of the IT structure, concerning scalability, is that it consumes a large amount of memory. In this paper, we address this limitation by proposing an improved data structure named MEIT (Memory Efficient Itemset Tree). It offers an efficient node compression mechanism for reducing IT node size. It also performs on-the-fly node decompression for restoring compressed information when needed. An experimental study with datasets commonly used in the data mining literature representing various types of data shows that MEIT are up to 60 % smaller than IT (43% on average).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MEIT: Memory Efficient Itemset Tree for Targeted Association Rule Mining

Fournier-Viger

Mwamikazi

Gueniche

et al. 2013

Advanced Data Mining and Applications

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“…5: Let |S| be the total number of time steps in a dataset. 6: Let L be a list of all itemsets containing the UDIIs. 7: Let LS master be a list of all large status patterns.…”

Section: Status Transferral From Itemset To Association Rulementioning

confidence: 99%

“…Targeted association mining utilizes the itemset tree data structure, which reduces the complexity of rule mining as compared to Apriori-like algorithms [4] [5]. Previous work by Lavergne et al [6] introduced the Ordered Min-Max Itemset Tree, which takes advantage of the ordered nature of the tree as well as the range of items contained within a given subtree to reduce the search path for a given query. The query sets, known as user defined interest itemsets (UDII), represent the user's interests.…”

Section: Introductionmentioning

confidence: 99%

DynTARM: An In-Memory Data Structure for Targeted Strong and Rare Association Rule Mining over Time-Varying Domains

Lavergne

Benton

Raghavan

et al. 2013

2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)

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Recently, with companies and government agencies saving large repositories of time stream/temporal data, there is a large push for adapting association rule mining algorithms for dynamic, targeted querying. In addition, issues with data processing latency and results depreciating in value with the passage of time, create a need for swifter and more efficient processing. The aim of targeted association mining is to find potentially interesting implications in large repositories of data. Using targeted association mining techniques, specific implications that contain items of user interest can be found faster and before the implications have depreciated in value beyond usefulness.In this paper, the DynTARM algorithm is proposed for the discovery of targeted and rare association rules. DynTARM has the flexibility to discover strong and rare association rules from data streams within the user's sphere of interest. By introducing a measure, called the Volatility Index, to assess the fluctuation in the confidence of rules, rules conforming to different temporal patterns are discovered.

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Interactive Discovery of Statistically Significant Itemsets

Fournier-Viger

Yao

et al. 2018

Lecture Notes in Computer Science

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Min-Max Itemset Trees for Dense and Categorical Datasets

Cited by 14 publications

References 6 publications

MEIT: Memory Efficient Itemset Tree for Targeted Association Rule Mining

MEIT: Memory Efficient Itemset Tree for Targeted Association Rule Mining

DynTARM: An In-Memory Data Structure for Targeted Strong and Rare Association Rule Mining over Time-Varying Domains

Interactive Discovery of Statistically Significant Itemsets

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