A new framework for mining frequent interaction patterns from meeting databases

Fariha, Anna; Ahmed, Chowdhury Farhan; Leung, Carson K.; Samiullah, M.; Pervin, Suraiya; Cao, Longbing

doi:10.1016/j.engappai.2015.06.019

Cited by 10 publications

(2 citation statements)

References 26 publications

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“…Mining ARs, including ARs, minimal non‐redundant association rules (MNARs), and most generalization association rules (MGARs), is a model being widely used in market basket analysis, online e‐commerce such as Amazon, Alibaba, and so on, and several other recommendation systems. Traditional approaches for mining ARs consist of two steps: mining frequent itemsets (FIs)/frequent closed itemsets (FCIs)/frequent maximal itemsets (FMIs) (FIs/FCIs/FMIs), and generating rules from those itemsets. Some variants of FIs such as high utility itemsets (itemsets whose utility satisfies a given threshold), top‐ k high utility itemsets (top‐k itemsets with highest utility), weighted pattern (pattern with weighted items), erasable itemsets (itemsets can be eliminated but do not greatly affect the factory's profit), weighted erasable patterns (erasable itemsets considered the distinct weight of each item), and so on are proposed.…”

Section: Introductionmentioning

confidence: 99%

The lattice‐based approaches for mining association rules: a review

2016

WIREs Data Min & Knowl

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The traditional methods for mining association rules (ARs) include two phrases: mining frequent itemsets (FIs)/frequent closed itemsets (FCIs)/frequent maximal itemsets (FMIs) and generating ARs from FIs/FCIs/FMIs. Lattice‐based approaches (LBAs) for mining ARs are new approaches including two phrases: frequent itemset lattice (FIL)/frequent closed itemset lattice (FCIL) building and generating ARs from the lattice. Total mining time of LBAs for mining ARs outperforms the traditional methods for mining ARs. Besides, the most important advantage of LBAs for mining ARs is that the algorithms only build the lattice once and mine ARs with many different confidences or many different minimum supports (the thresholds have to be greater than or equal to the threshold used to build lattices) without mining FIs/FCIs again. In this article, we describe a number of existing LBAs for mining ARs on static databases including lattice building and rule generation. In addition, in today's online system, the data often change in several operations such as insert, delete, and update. Hence, a number of LBAs for mining ARs on dynamic databases are mentioned. Finally, complexity analysis of the LBAs for mining ARs is also thoroughly discussed. WIREs Data Mining Knowl Discov 2016, 6:140–151. doi: 10.1002/widm.1181 This article is categorized under: Algorithmic Development > Association Rules

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Section: Introductionmentioning

confidence: 99%

The lattice‐based approaches for mining association rules: a review

2016

WIREs Data Min & Knowl

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“…Mining of frequent patterns [23], and 2. Formation of association rules (by using the mined frequent patterns as antecedents and consequences of the rules).…”

Section: Introductionmentioning

confidence: 99%

A Data Analytic Algorithm for Managing, Querying, and Processing Uncertain Big Data in Cloud Environments

Jiang

Leung

2015

Algorithms

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Big data are everywhere as high volumes of varieties of valuable precise and uncertain data can be easily collected or generated at high velocity in various real-life applications. Embedded in these big data are rich sets of useful information and knowledge. To mine these big data and to discover useful information and knowledge, we present a data analytic algorithm in this article. Our algorithm manages, queries, and processes uncertain big data in cloud environments. More specifically, it manages transactions of uncertain big data, allows users to query these big data by specifying constraints expressing their interests, and processes the user-specified constraints to discover useful information and knowledge from the uncertain big data. As each item in every transaction in these uncertain big data is associated with an existential probability value expressing the likelihood of that item to be present in a particular transaction, computation could be intensive. Our algorithm uses the MapReduce model on a cloud environment for effective data analytics on these uncertain big data. Experimental results show the effectiveness of our data analytic algorithm for managing, querying, and processing uncertain big data in cloud environments.

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MH-DAGMiner: maximal hierarchical sub-DAG mining in directed weighted networks

Tennakoon

Nayak

2018

Knowl Inf Syst

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A new framework for mining frequent interaction patterns from meeting databases

Cited by 10 publications

References 26 publications

The lattice‐based approaches for mining association rules: a review

The lattice‐based approaches for mining association rules: a review

A Data Analytic Algorithm for Managing, Querying, and Processing Uncertain Big Data in Cloud Environments

MH-DAGMiner: maximal hierarchical sub-DAG mining in directed weighted networks

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