UTARM: an efficient algorithm for mining of utility-oriented temporal association rules

Maragatham, G.; Lakshmi, M.

doi:10.1504/ijkedm.2015.071293

Cited by 8 publications

(6 citation statements)

References 35 publications

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“…The proposed approach has extended the UTARM algorithm [5] for extracting appliances' association support values to a certain time. This time can be an hour, a day, a week, a month, or a season.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…In the proposed work, appliance-appliance associations are represented by using association rules and hierarchical clustering. Association rules are extracted by employing the Utility-oriented Temporal Association Rules Mining (UTARM) algorithm [5]. Agglomerative hierarchical clustering is used to group appliances with similar usage behavior together.…”

Section: Introductionmentioning

confidence: 99%

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Mining Temporal Patterns to Discover Inter-Appliance Associations Using Smart Meter Data

Osama

Alfonse

Salem

2019

BDCC

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With the emergence of the smart grid environment, smart meters are considered one of the main key enablers for developing energy management solutions in residential home premises. Power consumption in the residential sector is affected by the behavior of home residents through using their home appliances. Respecting such behavior and preferences is essential for developing demand response programs. The main contribution of this paper is to discover the association between appliances’ usage through mining temporal association rules in addition to applying the temporal clustering technique for grouping appliances with similar usage at a particular time. The proposed method is applied on a time-series dataset, which is the United Kingdom Domestic Appliance-Level Electricity (UK-DALE), and the results that are achieved discovered appliance–appliance associations that have similar usage patterns with respect to the 24 h of the day.

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Section: Proposed Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mining Temporal Patterns to Discover Inter-Appliance Associations Using Smart Meter Data

Osama

Alfonse

Salem

2019

BDCC

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“…The proposed methodology has utilized the UTARM algorithm (Maragatham and Lakshmi, 2015) for extracting appliance-time association. The basic idea of using the UTARM algorithm is to extract significant temporal associations that are highly impacted by resident's usage taking into consideration the importance of using an appliance at a time in addition to identifying the lifespan of the discovered rule.…”

Section: Methodsmentioning

confidence: 99%

“…The proposed methodology employs the Utility-oriented Temporal Association Rules Mining (UTARM) algorithm to enhance the quality of the mined temporal associations considering the utility factor (Maragatham and Lakshmi, 2015). The algorithm is applied to the UK Domestic Appliance-Level Electricity (UK-DALE) dataset (Kelly and Knottenbelt, 2015).…”

Section: Introductionmentioning

confidence: 99%

An efficient algorithm for extracting appliance-time association using smart meter data

Osama

Alfonse

Salem

2019

Heliyon

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Demand Response (DR) programs play a significant role for developing energy management solutions. Gaining home residents trust and respecting their appliances usage preferences are essential factors for promoting these programs. Extracting resident's usage behaviour is a challenging task with the infinite massive amount of data being generated from smart meters. The main contribution of this paper is to extract temporal association patterns of energy consumption at appliance level. The proposed approach extends the Utility-oriented Temporal Association Rules Mining (UTARM) algorithm to discover appliances usage preference at a time. The results achieved from the proposed work succeeded to discover appliance-time association considering appliances usage priority as a utility factor with respect to the 24-hours of the day as a temporal partitioning factor.

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“…By defining minimum values for support and confidence, association rules are extracted from the database. For the A0B rule, support and confidence are defined as follows (Maragatham and Lakshmi, 2012):…”

Section: Association Rule Miningmentioning

confidence: 99%

Investigation of cinematic genre diversity based on social network analysis: the lost ring of the Iranian cinema industry

Noroozian

Amiri

Kermani

2022

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PurposeMovies critics believe that the diversity of Iranian cinematic genres has decreased over time. The paper aims to answer the following questions: What is the impact of the continuous cooperation between the key nodes on the audience's taste, uniformity of the cinematic genres and the box office? Is there any relationship between the importance of actors in the actors' network and their popularity?Design/methodology/approachIn the artistic world, artists' relationships lead to a network that affects individuals' commercial or artistic success and defines the artwork's value. To study the issue that the diversity of Iranian cinematic genres has decreased over time, the authors utilized social network analysis (SNA), in which every actor is considered a node, and its collaboration with others in the same movies is depicted via edges. After preparing the desired dataset, networks were generated, and metrics were calculated. First, the authors compared the structure of the network with the box office. The results illustrated that the network density growth negatively affects box office. Second, network key nodes were identified, their relationships with other actors were inspected using the Apriori algorithm to examine the density cause and the cinematic genre of key nodes, and their followers were investigated. Finally, the relationship between the actors' Instagram follower count and their importance in the network structure was analyzed to answer whether the generated network is acceptable in society.FindingsThe social problem genre has stabilized due to continuous cooperation between the core nodes because network density negatively impacts the box office. As well as, the generated network in the cinema is acceptable by the audience because there is a positive correlation between the importance of actors in the network and their popularity.Originality/valueThe novelty of this paper is investigating the issue raised in the cinema industry and trying to inspect its aspects by utilizing the SNA to deepen the cinematic research and fill the gaps. This study demonstrates a positive correlation between the actors' Instagram follower count and their importance in the network structure, indicating that people follow those central in the actors' network. As well as investigating the network key nodes with a heuristic algorithm using coreness centrality and analyzing their relationships with others through the Apriori algorithm. The authors situated the analysis using a novel and original dataset from the Iranian actors who participated in the Fajr Film Festival from 1998 to 2020.

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UTARM: an efficient algorithm for mining of utility-oriented temporal association rules

Cited by 8 publications

References 35 publications

Mining Temporal Patterns to Discover Inter-Appliance Associations Using Smart Meter Data

Mining Temporal Patterns to Discover Inter-Appliance Associations Using Smart Meter Data

An efficient algorithm for extracting appliance-time association using smart meter data

Investigation of cinematic genre diversity based on social network analysis: the lost ring of the Iranian cinema industry

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