Parallelization of the next Closure algorithm for generating the minimum set of implication rules

Moraes, Nilander R. M. de; Dias, Sérgio M.; Freitas, Henrique C.; Zárate, Luis Enrique

doi:10.5430/air.v5n2p40

Cited by 6 publications

(3 citation statements)

References 28 publications

(24 reference statements)

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“…Therefore, it is important to use tools that can simulate real data. It is also useful to compare and analyze results among algorithms, as realized in [de Moraes et al, 2016] [Santos et al, 2018.…”

Section: Scgaz -Synthetic Context Generatormentioning

confidence: 99%

Extracting concepts from triadic contexts using Binary Decision Diagram

Neves¹,

Zarate²,

Song³

2022

jucs

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Due to the high complexity of real problems, a considerable amount of research that deals with high volumes of information has emerged. The literature has considered new applications of data analysis for high dimensional environments in order to manage the difficulty in extracting knowledge from a database, especially with the increase in social and professional networks. Tri- adic Concept Analysis (TCA) is a technique used in the applied mathematical area of data analysis. Its main purpose is to enable knowledge extraction from a context that contains objects, attributes, and conditions in a hierarchical and systematized representation. There are several algorithms that can extract concepts, but they are inefficient when applied to large datasets because the compu- tational costs are exponential. The objective of this paper is to add a new data structure, binary decision diagrams (BDD), in the TRIAS algorithm and retrieve triadic concepts for high dimen- sional contexts. BDD was used to characterize formal contexts, objects, attributes, and conditions. Moreover, to reduce the computational resources needed to manipulate a high-volume of data, the usage of BDD was implemented to simplify and represent data. The results show that this method has a considerably better speedup when compared to the original algorithm. Also, our approach discovered concepts that were previously unachievable when addressing high dimensional contexts.

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Section: Scgaz -Synthetic Context Generatormentioning

confidence: 99%

Extracting concepts from triadic contexts using Binary Decision Diagram

Neves¹,

Zarate²,

Song³

2022

jucs

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“…Finally, draw the corresponding concept lattice in a top‐down, bottom‐up, or enumeration method (Dong et al, 2019; Zhang et al, 2019). Batch lattice construction algorithms can be found in (Andrews, 2009; Andrews, 2017; de Moraes, Dias, Freitas, & Zarate, 2016; Ganter, 2010; Kuznetsov, 1993; Muangprathub, 2014; Outrata & Vychodil, 2012). Incremental algorithms : construct the lattice incrementally by iterative processing each object/attribute from the formal context, then generate formal concepts and update edges accordingly.…”

Section: Preliminariesmentioning

confidence: 99%

A comprehensive review on updating concept lattices and its application in updating association rules

Shemis

Mohammed

2021

WIREs Data Min & Knowl

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Formal concept analysis (FCA) visualizes formal concepts in terms of a concept lattice. Usually, it is an NP‐problem and consumes plenty of time and storage space to update the changes of the lattice. Thus, introducing an efficient way to update and maintain such lattices is a significant area of interest within the field of FCA and its applications. One of those vital FCA applications is the association rule mining (ARM), which aims at generating a loss‐less nonredundant compact Association Rule‐basis (AR‐basis). Currently, the real‐world data rapidly overgrow that asks the need for updating the existing concept lattice and AR‐basis upon data change continually. Intuitively, updating and maintaining an existing concept‐lattice or AR‐basis is much more efficient and consistent than reconstructing them from scratch, particularly in the case of massive data. So far, the area of updating both concept lattice and AR‐basis has not received much attention. Besides, few noncomprehensive studies have focused only on updating the concept lattice. From this point, this article comprehensively introduces basic knowledge regarding updating both concept lattices and AR‐basis with new illustrations, formalization, and examples. Also, the article reviews and compares recent remarkable works and explores the emerging future research trends. This article is categorized under: Algorithmic Development > Association Rules Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Association Rules

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“…However, the time needed to extract implications from the contexts considered in our case study were not prohibitive due to the sparsity of the input data. For high-dimensional formal contexts, it is important to design or use a parallel or distributed version of the algorithm (de Moraes et al , 2016).…”

Section: Formal Concept Analysismentioning

confidence: 99%

Identification of substructures in complex networks using formal concept analysis

Neto

Dias²,

Missaoui

et al. 2018

IJWIS

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Purpose In recent years, the increasing complexity of the hyper-connected world demands new approaches for social network analysis. The main challenges are to find new computational methods that allow the representation, characterization and analysis of these social networks. Nowadays, formal concept analysis (FCA) is considered an alternative to identifying conceptual structures in a social network. In this FCA-based work, this paper aims to show the potential of building computational models based on implications to represent and analyze two-mode networks. Design/methodology/approach This study proposes an approach to find three important substructures in social networks such as conservative access patterns, minimum behavior patterns and canonical access patterns. The present study approach considered as a case study a database containing the access logs of a cable internet service provider. Findings The result allows us to uncover access patterns, conservative access patterns and minimum access behavior patterns. Furthermore, through the use of implications sets, the relationships between event-type elements (websites) in two-mode networks are analyzed. This paper discusses, in a generic form, the adopted procedures that can be extended to other social networks. Originality/value A new approach is proposed for the identification of conservative behavior in two-mode networks. The proper implications needed to handle minimum behavior pattern in two-mode networks is also proposed to be analyzed. The one-item conclusion implications are easy to understand and can be more relevant to anyone looking for one particular website access pattern. Finally, a method for a canonical behavior representation in two-mode networks using a canonical set of implications (steam base), which present a minimal set of implications without loss of information, is proposed.

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Parallelization of the next Closure algorithm for generating the minimum set of implication rules

Cited by 6 publications

References 28 publications

Extracting concepts from triadic contexts using Binary Decision Diagram

Extracting concepts from triadic contexts using Binary Decision Diagram

A comprehensive review on updating concept lattices and its application in updating association rules

Identification of substructures in complex networks using formal concept analysis

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