Mining Partially-Ordered Sequential Rules Common to Multiple Sequences

Fournier-Viger, Philippe; Wu, Chengwei; Tseng, Vincent S.; Cao, Longbing; Nkambou, Roger

doi:10.1109/tkde.2015.2405509

Cited by 63 publications

(42 citation statements)

References 29 publications

(55 reference statements)

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“…These rules are usually generated from sequential patterns or episodes (Febrer‐Hernández & Hernández‐Palancar, 2012; Fournier‐Viger, Lin, Kiran, Koh, & Thomas, 2017; Mooney & Roddick, 2013; Zimmermann, 2014) that appear in a single sequence (Mannila et al, 1997; Deogun & Jiang, 2005), across sequences (Lo, Ramalingam, Ranganath, & Vaswani, 2012; G. Das, Lin, Mannila, Renganathan, & Smyth, 1998), or common to multiple sequences (Lo et al, 2009; Fournier‐Viger, Wu, Tseng, Cao, & Nkambou, 2015). Classical algorithms are usually extended making use of a sliding‐window in which they find the frequent itemsets that will be used to generate the rules (Mannila et al, 1997; Fournier‐Viger, Wu, Tseng, & Nkambou, 2012).…”

Section: Considering Time As An Implied Component In the Mining Processmentioning

confidence: 99%

“…Finally, notice that some of the most used algorithms are ARMADA (Winarko & Roddick, 2007), CMRules (Fournier‐Viger, Faghihi, et al, 2012), and TRuleGrowth (Fournier‐Viger et al, 2015). In addition, the algorithms CM‐SPADE (Fournier‐Viger, Gomariz, Campos, & Thomas, 2014) and SPAM (Ayres, Flannick, Gehrke, & Yiu, 2002) are also widely used to extract sequential patterns and then to generate temporal rules from them.…”

Section: Real‐world Applications and Available Software Toolsmentioning

confidence: 99%

See 1 more Smart Citation

Temporal association rule mining: An overview considering the time variable as an integral or implied component

Segura‐Delgado

Gacto

Alcalá

et al. 2020

WIREs Data Min & Knowl

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Association rules are commonly used to provide decision‐makers with knowledge that helps them to make good decisions. Most of the published proposals mine association rules without paying particular attention to temporal information. However, in real‐life applications data usually change over time or presenting different temporal situations. Therefore, the extracted knowledge may not be useful, since we may not know whether the rules are currently applicable or whether they will be applicable in the future. For this reason, in recent years, many methods have been proposed in the literature for mining temporal association rules, which introduce a greater predictive and descriptive power providing an additional degree of interestingness. One of the main problems in this research field is the lack of visibility most works suffer since there is no standard terminology to refer to it, making it difficult to find and compare proposals and studies in the field. This contribution attempts to offer a well‐defined framework that allows researchers both to easily locate the previous proposals and to propose well‐grounded methods in the future. To accomplish both objectives, a two‐level taxonomy is proposed according to whether the time variable is considered to provide order to the data collection and to locate some temporal constraints, or whether it is considered as an attribute within the learning process. Some recent applications, available software tools, and a bibliographical analysis in accordance with the Web of Science are also shown. Finally, some critical considerations and potential further directions are discussed. This article is categorized under: Technologies > Association Rules Algorithmic Development > Association Rules

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Section: Considering Time As An Implied Component In the Mining Processmentioning

confidence: 99%

Section: Real‐world Applications and Available Software Toolsmentioning

confidence: 99%

Temporal association rule mining: An overview considering the time variable as an integral or implied component

Segura‐Delgado

Gacto

Alcalá

et al. 2020

WIREs Data Min & Knowl

View full text Add to dashboard Cite

show abstract

“…The main difference with association rule mining is the concept of time or sequence in sequences. A sequential rule X  Y can mean X followed by Y, while an association rule X  Y can mean that if X appears, then Y will also appear and can occur at one time [11].…”

Section: Sequential Rule Miningmentioning

confidence: 99%

Segmentation-Based Sequential Rules For Product Promotion Recommendations As Sales Strategy (Case Study: Dayra Store)

Ramadhan

Rokhman²

2020

Indonesian J. Comput. Cybern. Syst.

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One of the problems in the promotion is the high cost. Identifying the customer segments that have made transactions, sellers can promote better products to potential consumers. The segmentation of potential consumers can be integrated with the products that consumers tend to buy. The relationship can be found using pattern analysis using the Association Rule Mining (ARM) method. ARM will generate rule patterns from the old transaction data, and the rules can be used for recommendations. This study uses a segmented-based sequential rule method that generates sequential rules from each customer segment to become product promotion for potential consumers. The method was tested by comparing product promotions based on rules and product promotions without based on rules. Based on the test results, the average percentage of transaction from product promotion based on rules is 2,622%, higher than the promotion with the latest products with an average rate of transactions only 0,315%. The hypothesis in each segment obtained from the sample can support the statement that product promotion in all segments based on rules can be more effective in increasing sales compared to promotions that use the latest products without using rules recommendations.

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“…Peeraer and Van Petegem [13] explained that province provides a warning on the additional impact of appropriate parameters at the teacher education level on ICT usage. Fournier-Viger et al [14] presented a rule growth approach to predict the performance of students in e-learning. Compared to normal sequential algorithms, the prediction accuracy of rule-growth algorithms is improved.…”

Section: Related Workmentioning

confidence: 99%

ICT Utilization and Improving Students Performance in Higher Education

Pathak¹,

Raja²,

Sharma³

et al. 2019

IJRTE

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Nowadays, higher education providing institutions depend upon Information and Communication Technologies (ICT) for all purposes like teaching, administration, learning and other research related works. ICT is found to be a major resource for bringing competitive growth for an organization. In general, the use of ICT in higher education institution plays a vital role in providing modern learning approaches. Further, the utilization of data mining in ICT can predict the performance of students and offer a way to improve it. This paper identifies the usage of ICTs among higher graduates and the impact of parameters such as gender, area of interest as well as name of the university. In addition, data mining based ranking algorithm is introduced in order to analyze the performance of students. The evaluation results show that the use of ICT in higher education provides technological improvements and these enhancements are associated with factors such as gender, area of interest as well as university. Moreover, the ranking algorithm can easily evaluate the ICT based performance of higher education institutions.

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Mining Partially-Ordered Sequential Rules Common to Multiple Sequences

Cited by 63 publications

References 29 publications

Temporal association rule mining: An overview considering the time variable as an integral or implied component

Temporal association rule mining: An overview considering the time variable as an integral or implied component

Segmentation-Based Sequential Rules For Product Promotion Recommendations As Sales Strategy (Case Study: Dayra Store)

ICT Utilization and Improving Students Performance in Higher Education

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