DCE -miner: an association rule mining algorithm for multimedia based on the MapReduce framework

Li, Chengyan; Feng, Shixiang; Sun, Guanglu

doi:10.1007/s11042-019-08361-y

Cited by 10 publications

(5 citation statements)

References 25 publications

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“…When designing the image modeling of the environmental protection golden furnace, we should pay special attention to the product form. [26]. rough association rules, we can effectively mine the dependence and association between one thing and other things.…”

Section: Factormentioning

confidence: 99%

Application of Factor Analysis in Image Modeling Design of Environmental Protection Gold Stove

Zhou

2022

Mathematical Problems in Engineering

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In order to design an environmentally friendly gold furnace that considers both traditional cultural elements and modern decorative elements, this paper puts forward the application of factor analysis in the image modeling design of an environmentally friendly gold furnace. Firstly, the elements of lines, colors, and patterns were extracted by factor analysis. By using the hierarchical structure model and factor analysis, the synchronous parameter fusion model of image modeling design factors of the environmental protection gold furnace is constructed to fuse the output reliability parameters of image modeling of the environmental protection gold furnace and adjust them adaptively. The factor analysis and process control of image modeling of environmental protection golden stoves are carried out using the method of differential feature analysis. Then, the Atlas and image Kanban are analyzed, and the image design factors of the environmental protection golden stove are excavated. Finally, the concepts of cultural space and cultural time are introduced. Based on the study of historical and material materials, the overall comparative analysis is carried out from three aspects: shape, color, and pattern. Combined with the extracted target image and the excavated design factors, the image modeling design of the environmental protection gold furnace is effectively carried out, providing design methods and sample references for the innovative design of the environmental protection gold furnace. The experimental results show that the mining design factors can effectively analyze the problems existing in the modeling design of the current environmental protection gold furnace, with a low bit error rate and strong practicability.

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Section: Factormentioning

confidence: 99%

Application of Factor Analysis in Image Modeling Design of Environmental Protection Gold Stove

Zhou

2022

Mathematical Problems in Engineering

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“…Support is related to historical data capacity to establish a rule; confidence, how a certain rule works, and the lift is the ratio between confidence and support. There are several algorithms for association rule mining, such as Apriori [1], FPGrowth [25], Predictive Apriori [47], FPMax [24], DC-miner [7], and FIUFP-Growth [52]. The input is a collection of transactions (in this work, a collection of T), and each transaction contains one or more items (i.e., positional data) obtained from a (1)…”

Section: Generation Of Association Rulesmentioning

confidence: 99%

Sport action mining: Dribbling recognition in soccer

Barbon

Pinto

Barroso

et al. 2021

Multimed Tools Appl

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Recent advances in Computer Vision and Machine Learning empowered the use of image and positional data in several high-level analyses in Sports Science, such as player action classification, recognition of complex human movements, and tactical analysis of team sports. In the context of sports action analysis, the use of positional data allows new developments and opportunities by taking into account players' positions over time. Exploiting the positional data and its sequence in a systematic way, we proposed a framework that bridges association rule mining and action recognition. The proposed Sports Action Mining (SAM) framework is grounded on the usage of positional data for recognising actions, e.g., dribbling. We hypothesise that different sports actions could be modelled using a sequence of confidence levels computed from previous players' locations. The proposed method takes advantage of an association rule mining algorithm (e.g., FPGrowth) to generate displacement sequences for modelling actions in soccer. In this context, transactions are sequences of traces representing player displacements, while itemsets are players' coordinates on the pitch. The experimental results pointed out the Random Forest classifier achieved a balanced accuracy value of 93.3% for detecting dribbling actions, which are considered complex events in soccer. Additionally, the proposed framework provides insights on players' skills and player's roles based on a small amount of positional data.

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“…Performance of each classifiers separately, as well as all classifiers combined were discussed. In [49], a fast association rule mining algorithm with low memory requirements based on the MapReduce framework was proposed. The work in [50] defined a new concept that produces multitask rules.…”

Section: Related Researchmentioning

confidence: 99%

An Efficient Association Rule Mining From Distributed Medical Databases for Predicting Heart Diseases

et al. 2021

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Electronic Health Records (EHRs) are aggregated, combined and analyzed for suitable treatment planning and safe therapeutic procedures of patients. Integrated EHRs facilitate the examination, diagnosis and treatment of diseases. However, the existing EHRs models are centralized. There are several obstacles that limit the proliferation of centralized EHRs, such as data size, privacy and data ownership consideration. In this paper, we propose a novel methodology and algorithm to handle the mining of distributed medical data sources at different sites (hospitals and clinics) using Association Rules. These medical data resources cannot be moved to other network sites. Therefore, the desired global computation must be decomposed into local computations to match the distribution of data across the network. The capability to decompose computations must be general enough to handle different distributions of data and different participating nodes in each instance of the global computation. In the proposed methodology, each distributed data source is represented by an agent. The global association rule computation is then performed by the agent either exchanging some minimal summaries with other agents or travelling to all the sites and performing local tasks that can be done at each local site. The objective is to perform global tasks with a minimum of communication or travel by participating agents across the network, this will preserve the privacy and the security of the local data. The proposed association rule mining methodology will be used for heart disease prediction using real heart disease data. These real data exist at different clinics and cannot be moved to a central site. The proposed model protects the patient data privacy and achieves the same results as if the data are moved and joined at a central site. We also validate the extracted association rules from all the data providers using an independent test datasets.INDEX TERMS Association rules, medical distributed databases, electronic health care records, agents, data privacy.

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DCE -miner: an association rule mining algorithm for multimedia based on the MapReduce framework

Cited by 10 publications

References 25 publications

Application of Factor Analysis in Image Modeling Design of Environmental Protection Gold Stove

Application of Factor Analysis in Image Modeling Design of Environmental Protection Gold Stove

Sport action mining: Dribbling recognition in soccer

An Efficient Association Rule Mining From Distributed Medical Databases for Predicting Heart Diseases

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