The Evolution of the Association Rules

Jaiswal, Varshali; Agarwal, Jitendra

doi:10.7763/ijmo.2012.v2.220

Cited by 8 publications

(7 citation statements)

References 1 publication

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“…4,7,11,14,25,26,33,34,35,42,45,46,47,48,49,50,51,53,54,62,64,65,69,71,74,75,76,77,78,79,81,82,84,89,93,102,107,108,109 EQU. 8,19,30,31,36,38,44,59,60,85,95,96 SYM. 2,3,12,14,15,26,27,29,…”

Section: Classification Of Interestingness Measuresmentioning

confidence: 99%

Classification of objective interestingness measures

Phan¹,

Phan²,

Phan³

et al. 2016

EAI Endorsed Transactions on Context-aware Systems and Applicat

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The creation of the interestingness measures for evaluating the quality of the association rule-based knowledge plays an important role in the post-processing of the Knowledge Discovery from Databases. More and more interestingness measures are proposed by two approaches (subjective assessment and objective assessment), studying the properties or the attributes of the interestingness measures is important in understanding the nature of the objective interestingness measures. In this paper, we focus primarily on the objective interestingness measures to obtain a general view of recent researches on the nature of the objective interestingness measures, as well as complete a new classification on 109 selected objective interestingness measures on 6 criterions (independence, equilibrium, symmetry, variation, description, and statistics).

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“…4,7,11,14,25,26,33,34,35,42,45,46,47,48,49,50,51,53,54,62,64,65,69,71,74,75,76,77,78,79,81,82,84,89,93,102,107,108,109 EQU. 8,19,30,31,36,38,44,59,60,85,95,96 SYM. 2,3,12,14,15,26,27,29,…”

Section: Classification Of Interestingness Measuresmentioning

confidence: 99%

Classification of objective interestingness measures

Phan¹,

Phan²,

Phan³

et al. 2016

EAI Endorsed Transactions on Context-aware Systems and Applicat

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“…The Apriori [25], an association rule mining algorithm, was used to find movement patterns of birds and species with periodic collective movement [26]. Association rule mining is a method for discovering interesting relationships between variables in large data sets [27]. Association rules have been used in several applications to find frequent patterns in the data, mainly in the discovery of relationships between products in market basket analysis [28] to guide marketing actions and provides statistical measures of correlation and dependency between the associations.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we proposed a framework that uses trajectory analysis and association rule mining [27] to provide statistical measures of correlation and dependence between associations and can be used to determine the relationship level between animals, their social interactions, and their interactions with other environmental factors based on their individual behavior and movement data. The higher the frequency of cooccurrence of these animals is, the greater the likelihood of interaction between them.…”

Section: Introductionmentioning

confidence: 99%

“…However, we observed a lack of research proposing a unified solution that aggregates resources for analyses of individual animal behavior and of social interactions between animals. The primary scientific contribution of this work is to present a framework that uses trajectory analysis and association rule mining [Jaiswal and Agarwal, 2012] to provide statistical measures of correlation and dependence to determine the relationship level between animals, their social interactions, and their interactions with other environmental factors based on their individual behavior and movement data. We demonstrate the usefulness of the framework by applying it to movement data from jaguars in the Pantanal, Brazil.…”

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confidence: 99%

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Jaguar movement behavior: using trajectories and association rule mining algorithms to unveil behavioral states and social interactions

et al. 2021

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Animal movement data are widely collected with devices such as sensors and collars, increasing the ability of researchers to monitor animal movement and providing information about animal behavioral patterns. Animal behavior is used as a basis for understanding the relationship between animals and the environment and for guiding decision-making by researchers and public agencies about environmental preservation and conservation actions. Animal movement and behavior are widely studied with a focus on identifying behavioral patterns, such as, animal group formation, the distance between animals and their home range. However, we observed a lack of research proposing a unified solution that aggregates resources for analyses of individual animal behavior and of social interactions between animals. The primary scientific contribution of this work is to present a framework that uses trajectory analysis and association rule mining [Jaiswal and Agarwal, 2012] to provide statistical measures of correlation and dependence to determine the relationship level between animals, their social interactions, and their interactions with other environmental factors based on their individual behavior and movement data. We demonstrate the usefulness of the framework by applying it to movement data from jaguars in the Pantanal, Brazil. This allowed us to describe jaguar behavior, social interactions among jaguars and their behavior in different landscapes, thus providing a highly detailed investigation of jaguar movement decisions at the fine scale.

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“…In the classical framework, an association rule is considered to be interesting if its support (s) and confidence (c) exceed some userdefined minimum thresholds [13]. Support is defined as the percentage of transactions in the data that contain all items in both the antecedent and the consequent of the rule; that is, ( ∩ ) = { ∩ }/{ } [14]. Confidence on the other hand is an estimate of the conditional probability of given ; that is, ( ∩ )/ ( ) [13].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

A Study of K-ISMS Fault Analysis for Constructing Secure Internet of Things Service

Kim

Lim

Lee

2015

International Journal of Distributed Sensor Networks

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Although Internet of Things (IoT) technologies and services are being developed rapidly worldwide, concerns of potential security threats such as privacy violation, information leak, and hacking are increasing as more various sensors are connected to the Internet. There is a need for the study of introducing risk management and existing security management standard (e.g., ISO27001) to ensure the stability and reliability of IoT services. K-ISMS is a representative certification system that evaluates the security management level of the enterprise in Korea and is possible to apply as a standardized process to enhance the security management of IoT services. However, there are growing concerns about the quality deterioration of the K-ISMS certification assessment these days because of internet security incidents occurring frequently in K-ISMS certified enterprises. Therefore, various researches are required to improve the accuracy and objectivity of the certification assessment. Since existing studies mainly focus on simple statistical analysis of the K-ISMS assessment results, analysis on the cause of certification assessment fault based on past data analysis is insufficient. As a method of managing the certification inspection quality, in this paper, we analyze the association among the fault items of the K-ISMS certification assessment results using association rule mining which involves identifying an association rule among items in the database.

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The Evolution of the Association Rules

Cited by 8 publications

References 1 publication

Classification of objective interestingness measures

Classification of objective interestingness measures

Jaguar movement behavior: using trajectories and association rule mining algorithms to unveil behavioral states and social interactions

A Study of K-ISMS Fault Analysis for Constructing Secure Internet of Things Service

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