Assessing web sites quality: A systematic literature review by text and association rules mining

Rekik, Rim; Kallel, Ilhem; Casillas, Jorge; Alimi, Adel M.

doi:10.1016/j.ijinfomgt.2017.06.007

Cited by 80 publications

(36 citation statements)

References 35 publications

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“…Nearly all of the AI systems mentioned up to year 2000 use rule-based inference, and this element of AI techniques remains with us today; three of the specific AI systems in articles from 2017 and 2018 (Araujo & Pestana, 2017;Kao et al, 2017;Rekik, Kallel, Casillas, & Alimi, 2018) are also rule-based.…”

Section: Rule-based Inferencementioning

confidence: 99%

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Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda

Duan

Edwards

Dwivedi

2019

International Journal of Information Management

1,533

554

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Artificial intelligence (AI) has been in existence for over six decades and has experienced AI winters and springs. The rise of super computing power and Big Data technologies appear to have empowered AI in recent years. The new generation of AI is rapidly expanding and has again become an attractive topic for research. This paper aims to identify the challenges associated with the use and impact of revitalised AI based systems for decision making and offer a set of research propositions for information systems (IS) researchers. The paper first provides a view of the history of AI through the relevant papers published in the International Journal of Information Management (IJIM). It then discusses AI for decision making in general and the specific issues regarding the interaction and integration of AI to support or replace human decision makers in particular. To advance research on the use of AI for decision making in the era of Big Data, the paper offers twelve research propositions for IS researchers in terms of conceptual and theoretical development, AI technology-human interaction, and AI implementation.

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Section: Rule-based Inferencementioning

confidence: 99%

“…The main change over time is that originally the rules were usually elicited from human experts by a human knowledge engineer, whereas now they are more likely to have been developed using an automated method such as CART (Classification and Regression Trees) (Kao et al, 2017) or association rule mining (Rekik et al, 2018).…”

Section: Rule-based Inferencementioning

confidence: 99%

Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda

Duan

Edwards

Dwivedi

2019

International Journal of Information Management

1,533

554

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“…In association rule mining, three fundamental concepts are important, including an item, an item set, and a transaction [27].…”

Section: Fundamental Concepts Of Association Rule Miningmentioning

confidence: 99%

“…Second, according to the discovered item sets, a set of strong association rules are mined. e details on the a priori algorithm can be found in Reference [27,28]. Additionally, the a priori algorithm was executed by Waikato Environment for Knowledge Analysis (WEKA), an open-source tool, universally applied in business, education, and research [29].…”

Section: Miners Of Association Rulesmentioning

confidence: 99%

Research on Relationships among Different Distress Types of Asphalt Pavements with Semi‐Rigid Bases in China Using Association Rule Mining: A Statistical Point of View

Jing

Liu

Yang

et al. 2019

Advances in Civil Engineering

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Distress types are significant for asphalt pavement maintenance decision, and relationships among them can greatly influence the decision outcomes. In this study, to analyze the relationships among different distress types from a statistical point of view, 282 asphalt pavements with semirigid base structures in 23 regions of China were surveyed to identify 12 distress types, which were subsequently investigated by association rule mining. Results show that the distress types can be categorized into independent distress types (IDDTs), dependent distress types (DDTs), and rutting secondary distress types (RSDTs) based on the relationships. The relationships among IDDTs are the strongest, and those between IDDTs and DDTs, between rutting and RSDTs, and among depression, pumping, and raveling are also strong, while the others are weak. The weak relationships should be ignored, while the strong ones should be considered to reduce cost and guarantee accuracy of maintenance decision. The IDDTs are the most important distress types, so preventing them from occurring or maintaining them immediately can greatly preserve good performance of asphalt pavements. To help analyze the relationships, a distress-type system was established and verified. In conclusion, this work provides new insights to understand distress types.

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“…In a set of transactions, ARM focuses on finding associations between frequent items, enabling researchers to predict the frequency of one itemset depending on the frequency of another item in a transaction. The ARM technique has been extensively used in several applications, including privacy preservation [1], market-basket transaction data analysis [2], recommendation [3], health care [4,5], prediction [6], pattern finding in web browsing [7], ranking of text documents [8], and hazard identification [9], among others. The extraction of association rules from transaction datasets was initiated in 1993 [10].…”

Section: Introductionmentioning

confidence: 99%

Efficient Association Rules Hiding Using Genetic Algorithms

Bux

Wang

et al. 2018

Symmetry

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In today’s world, millions of transactions are connected to online businesses, and the main challenging task is ensuring the privacy of sensitive information. Sensitive association rules hiding (SARH) is an important goal of privacy protection algorithms. Various approaches and algorithms have been developed for sensitive association rules hiding, differentiated according to their hiding performance through utility preservation, prevention of ghost rules, and computational complexity. A meta-heuristic algorithm is a good candidate to solve the problem of SARH due to its selective and parallel search behavior, avoiding local minima capability. This paper proposes simple genetic encoding for SARH. The proposed algorithm formulates an objective function that estimates the effect on nonsensitive rules and offers recursive computation to reduce them. Three benchmark datasets were used for evaluation. The results show an improvement of 81% in execution time, 23% in utility, and 5% in accuracy.

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Assessing web sites quality: A systematic literature review by text and association rules mining

Cited by 80 publications

References 35 publications

Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda

Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda

Research on Relationships among Different Distress Types of Asphalt Pavements with Semi‐Rigid Bases in China Using Association Rule Mining: A Statistical Point of View

Efficient Association Rules Hiding Using Genetic Algorithms

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