Improved algorithm for parallel mining collaborative frequent itemsets in multiple data streams

Fang-ai, Liu; Wang, Qianqian; Wang, Xin

doi:10.1007/s10586-018-1859-y

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…Wang. Q and X Wang [26] developed Parallel Mining Collaborative frequent itemsets in Multiple Data stream (PMCMD-Stream). Two algorithms are developed to generate and analyze the potential frequent itemsets from the streams of data.…”

Section: Literature Surveymentioning

confidence: 99%

“…These methods are using some filtering constraints to find Frequent Patterns (FP) [35], [36], [37], [38]. Although this is a challenging task to find the use pattern, and different patterns carry different importance [23], [24], [25], [26]. In this research, a scheme is proposed based on the FP tree to extract significant information from the dataset.…”

Section: Introductionmentioning

confidence: 99%

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Frequent Pattern Retrieval on Data Streams by using Sliding Window

Kumar¹,

Rao²

2018

EAI Endorsed Transactions on Energy Web

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In different applications like recommender frameworks and market examination, regular patterns play a significant role in useful mining data. Mining regular patterns from sliding windows over streaming information has become a complex task. In this examination, the sliding window is utilized to build the framework and FP tree applied to mine the dataset's valuable data. The sliding window has the arrangement of patterns put away in the Matrix, which contains the transaction in the sliding information and then applied to the FP tree. In this paper, the Frequent Pattern Retrieval strategy is planned by utilizing an FP tree approach and a sliding window model to extract noteworthy examples from data streams. The proposed technique accomplished less runtime with low memory use for the Breast disease dataset and different datasets to run the least utility edge contrasted with different existing procedures.

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Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Frequent Pattern Retrieval on Data Streams by using Sliding Window

Kumar¹,

Rao²

2018

EAI Endorsed Transactions on Energy Web

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Implementation of English “Online and Offline” Hybrid Teaching Recommendation Platform Based on Reinforcement Learning

Dong

Wu²

2021

Security and Communication Networks

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At present, there is a serious disconnect between online teaching and offline teaching in English MOOC large-scale hybrid teaching recommendation platform, which is mainly due to the problems of cold start and matrix sparsity in the recommendation algorithm, and it is difficult to fully tap the user's interest characteristics because it only considers the user's rating but neglects the user's personalized evaluation. In order to solve the above problems, this paper proposes to use reinforcement learning thought and user evaluation factors to realize the online and offline hybrid English teaching recommendation platform. First, the idea of value function estimation in reinforcement learning is introduced, and the difference between user state value functions is used to replace the previous similarity calculation method, thus alleviating the matrix sparsity problem. The learning rate is used to control the convergence speed of the weight vector in the user state value function to alleviate the cold start problem. Second, by adding the learning of the user evaluation vector to the value function estimation of the state value function, the state value function of the user can be estimated approximately and the discrimination degree of the target user can be reflected. Experimental results show that the proposed recommendation algorithm can effectively alleviate the cold start and matrix sparsity problems existing in the current collaborative filtering recommendation algorithm and can dig deep into the characteristics of users' interests and further improve the accuracy of scoring prediction.

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A Method of Recommending Physical Education Network Course Resources Based on Machine Learning Algorithms

Wei

2021

Security and Communication Networks

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Aiming at the problem of difficult selection of physical education online course resources, a method of recommending online course resources based on machine learning algorithms is proposed. The information recommendation model is established through the expression of a collaborative filtering algorithm and resource feedback matrix. According to the feedback score of any user on the same data resource in the project set, the interest matching degree is established by comparative analysis, and the matching degree is substituted into the cosine similarity function to calculate the similarity threshold between each item and so on, calculate the similarity threshold number of all items, select the project resource that best matches the user according to the threshold number, and complete the recommendation. The experimental results show that the recommended method of physical education network curriculum resources based on machine learning algorithm is relatively excellent in recommendation accuracy and efficiency; this method can realize the innovation of higher physical education network curriculum teaching mode.

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Improved algorithm for parallel mining collaborative frequent itemsets in multiple data streams

Cited by 7 publications

References 23 publications

Frequent Pattern Retrieval on Data Streams by using Sliding Window

Frequent Pattern Retrieval on Data Streams by using Sliding Window

Implementation of English “Online and Offline” Hybrid Teaching Recommendation Platform Based on Reinforcement Learning

A Method of Recommending Physical Education Network Course Resources Based on Machine Learning Algorithms

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