Parallel and Distributed Association Rule Mining Algorithms: A Recent Survey

Biswas, Sudarsan; Biswas, Neepa; Mondal, Kartick Chandra

doi:10.26480/imcs.01.2019.15.24

Cited by 3 publications

(2 citation statements)

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“…Memory and CPU resources are unfortunately very limited, which make execution of the algorithm inefficient with low performance. Thus, the proposed method use multi-processing to execute Apriori algorithm in parallel [31], [32] with the following steps of parallel mining to get frequent events for each user datasets: i) define number of parallel process to run as N process, ii) divide the input file into different N parts for parallel processing, iii) start function that calculated the frequent dataset parallel, and iv) create the pool object to start mapping the input to different process object and join parts.…”

Section: Find Frequent Events In Parallelmentioning

confidence: 99%

Context-aware recommender system for multi-user smart home

Sobhy

Mohamed

Keshk

et al. 2023

IJECE

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<span lang="EN-US">Smart home is one of the most important applications of the internet of things (IoT). Smart home makes life simpler, easier to control, saves energy based on user’s behavior and interaction with the home appliances. Many existing approaches have designed a smart home system using data mining algorithms. However, these approaches do not consider multiusers that exist in the same location and time (which needs a complex control). They also use centralized mining algorithm, then the system’s efficiency is reduced when datasets increase. Therefore, in this paper, we firstly build a context-aware recommender system that considers multi-user’s preferences and solves their conflicts by using unsupervised algorithms to deliver useful recommendation services. Secondly, we improve smart home’s responsive using parallel computing. The results reveal that the proposed method is better than existing approaches.</span>

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Section: Find Frequent Events In Parallelmentioning

confidence: 99%

Context-aware recommender system for multi-user smart home

Sobhy

Mohamed

Keshk

et al. 2023

IJECE

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“…The power system is a very complex and highly nonlinear dynamic system, and its frequency calculation problem involves complex high-dimensional differential algebraic equations [9]. In addition, the traditional BP neural network is prone to local optimization in training, which hinders its in-depth study [10][11][12]. In recent years, the rapid development of deep learning provides a new direction and train of thought for frequency dynamic analysis of power system.…”

Section: Introductionmentioning

confidence: 99%

Power system Frequency Prediction after disturbance based on Deep Learning

Huang

2020

International Journal of Circuits, Systems and Signal Processing

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In order to ensure the safe and stable operation of power system, enrich the means of power grid analysis and control, and expand the application of deep intelligent learning methods in power grid systems, the application of deep learning intelligent machine learning method in frequency prediction of large power grid is explored. First, on the basis of deep learning, the frequency response mode of large power grid is analyzed and the key characteristic quantities that affect the frequency response mode are extracted. Second, the deep belief neural network (DBN. DNN) frequency prediction model is constructed. Also, the training and testing of the model are introduced. Finally, the input and output based on the DBN.CNN prediction model and the network structure design of the model are analyzed. The prediction performance of the model is evaluated. The results show that when the number of neurons in the hidden layer is 50, the model achieves the optimal prediction effect. Increasing the number of training samples helps to improve the modeling ability and prediction accuracy of the model. For frequency prediction problems, the number of training samples should be set to ≥400, and the number of hidden layers corresponding to the model should be 5. When the number of hidden layer neurons is 10, the prediction accuracy of the DBN/DNN network is poor. When the number of hidden layer neurons is 50, the model can achieve the best prediction effect. Overall, the DBN.DNN prediction model has good prediction performance. The RMSE of the forecast data is O. 0073Hz can basically meet the actual application requirements. Therefore, the frequency prediction method based on deep belief neural network has certain advantages in accuracy and efficiency.

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A State-of-the-Art Association Rule Mining Survey and its Rare Application, Challenges, Progress

Biswas¹,

Saha²,

Pandit³

2023

Int. J. Artif. Intell. Tools

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In recent years, association rule mining is exploring its popularity in the rule mining research communities. In this paper, we present the taxonomy of association patterns analysis approaches in depth. The association patterns analysis is based on support constraints, interestingness, and patterns dimension from different levels of abstraction. An in-depth research statistical view is presented in each category of the association patterns analysis research. Among the identified association rule mining research categories, we have selected rare pattern mining for further exploration of our study. Analysis of rare patterns is very much helpful in determining the rare relationships between patterns. Also, we will identify novel rare data patterns analysis techniques to solve the various problems in real-life applications. The overall research impact in the association rule mining research domain can be found in this review article. It will focus on various research scopes and the future directions of rule mining as well as rare pattern mining research.

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Parallel and Distributed Association Rule Mining Algorithms: A Recent Survey

Cited by 3 publications

References 0 publications

Context-aware recommender system for multi-user smart home

Context-aware recommender system for multi-user smart home

Power system Frequency Prediction after disturbance based on Deep Learning

A State-of-the-Art Association Rule Mining Survey and its Rare Application, Challenges, Progress

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