In recent years, with the continuous development of computer application technology, network technology, data storage technology, and the large amount of investment in information technology, enterprises have accumula...
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In recent years, with the continuous development of computer application technology, network technology, data storage technology, and the large amount of investment in information technology, enterprises have accumulated a large amount of data while transforming and improving enterprise management modes and means. How to mine useful data, discover important knowledge and extract useful information has become a hot topic of current research. Industrial big data is significantly different from traditional big data. The traditional big data is based on the Internet environment. Although the data has a high degree of discretization and distribution, its association is relatively simple. The collection of industrial process data is relatively easy, but the mathematical and physical and chemical mechanism models involved make the inherent relationship of data complex, so it is difficult to use common analytical models and methods for processing. In this paper, we propose a complex industrial automation data stream miningalgorithm based on random internet of robotic things, and experimental results show that the proposed algorithm has higher datamining efficiency and robustness.
datamining has become of great importance owing to ever-increasing amounts of data collected by large organizations. This paper propose an data mining algorithm called Ant-Miner(I), which is based on an improvement o...
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ISBN:
(纸本)354027894X
datamining has become of great importance owing to ever-increasing amounts of data collected by large organizations. This paper propose an data mining algorithm called Ant-Miner(I), which is based on an improvement of Ant Colony System(ACS) algorithm. Experimental results show that Ant-Miner(l) has a higher predictive accuracy and much smaller rule list than the original Ant-Miner algorithm.
Adverse drug events (ADEs) detection is the critical concern in the field of pharmacovigilance, and it is also necessary to optimize the ADEs prediction to reduce the drugrelated morbidity and mortality. Here we propo...
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ISBN:
(纸本)9781728116976
Adverse drug events (ADEs) detection is the critical concern in the field of pharmacovigilance, and it is also necessary to optimize the ADEs prediction to reduce the drugrelated morbidity and mortality. Here we propose a novel methods of data mining algorithms directed predictive pharmacosafety networks (PPNs) to compare their predictive performance and investigate the differences between data mining algorithms. The combinations of 152 cancer drugs and 633 ADEs in the 2010 FDA Adverse Event Reporting System(FAERS) data is the training data, and 2011-2015 FAERS data is the validation data. We find that performance of empirical Bayes geometric mean (EBGM) is closer to proportional reporting ratio (PRR), and greater than reporting odds ratio (ROR) in ADE detection. Further, only information component (IC) directed the PPNs have better predictive performance comparing to other data mining algorithms, the predictive performance of which reaches to AUROC=0.908 comparing to the existing AUROC=0.823, and the performance of IC is greater than EBGM in ADE detection.
This article proposed a supplier management decision support system based on data mining algorithms, aiming to help enterprises select the best supplier to improve procurement efficiency and reduce procurement costs. ...
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In the fiercely competitive realm of sports and physical education, the application of data mining algorithms has emerged as a vital solution. Machine learning has streamlined processes, offering a seamless means of e...
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In the fiercely competitive realm of sports and physical education, the application of data mining algorithms has emerged as a vital solution. Machine learning has streamlined processes, offering a seamless means of elevating the quality of education and training provided to students, particularly in the context of sports. This technological support empowers the sports education system to make more informed decisions pertaining to the physical development of aspiring athletes. In this comprehensive study, a blended approach of qualitative methods has been leveraged to gather intricate insights, enriching the overall understanding of the subject. Additionally, an in-depth exploration of articles and journals has been undertaken to scrutinize the practical implementation of dataalgorithm techniques geared towards enhancing physical training. The resultant findings underscore a substantial and tangible nexus between dataalgorithms and the domain of sports education. Of paramount significance is the central role played by data mining algorithms in augmenting performance. Notably, the National Sports Board (NSB) has extensively harnessed this technology to meticulously monitor players' on-field performance, ultimately leading to a granular comprehension of each player's capabilities. This paper emphasizes the methods of optimizing mistake detection and its joining systems for increasing the punishment in the operational procedures.
Smart Energy Management Systems (SEMS) have become indispensable in Micro-Grid (MG) infrastructure for saving energy usage costs and system control considering the time-varying parameters. In this paper, a new multi-s...
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Smart Energy Management Systems (SEMS) have become indispensable in Micro-Grid (MG) infrastructure for saving energy usage costs and system control considering the time-varying parameters. In this paper, a new multi-stage SEMS architecture is proposed for optimal energy management in MGs considering various resource uncertainties. The proposed SEMS performs various tasks such as data acquisition/mining/refinement, pattern recognition, learning parameters and offline/online decision making. To meet the energy consumption suitably, the multi-objective SEMS operates in multi-stage scheduling problem, i.e. day-ahead, hour-ahead, and real-time markets. Moreover, some data mining algorithms have been applied to reduce the huge amount of raw data, to recognize patterns for analysis, and to learn the given parameters. From the stochastic point of view, the proposed architecture also takes into account the uncertainties of weather conditions, energy consumption and the spot market price in the risk analysis. To handle these uncertainties, a stochastic scheduling approach which includes the mean and variance of energy cost is considered in the optimization process. The simulation results illustrate the efficiency of the proposed SEMS in different case studies. (C) 2016 Elsevier Ltd. All rights reserved.
Frequent pattern mining on data streams is of interest recently. However, it is not easy for users to determine a proper frequency threshold. It is more reasonable to ask users to set a bound on the result size. We st...
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Frequent pattern mining on data streams is of interest recently. However, it is not easy for users to determine a proper frequency threshold. It is more reasonable to ask users to set a bound on the result size. We study the problem of mining top K frequent itemsets in data streams. We introduce a method based on the Chernoff bound with a guarantee of the output quality and also a bound on the memory usage. We also propose an algorithm based on the Lossy Counting algorithm. In most of the experiments of the two proposed algorithms, we obtain perfect solutions and the memory space occupied by our algorithms is very small. Besides, we also propose the adapted approach of these two algorithms in order to handle the case when we are interested in mining the data in a sliding window. The experiments show that the results are accurate.
Big data has the characteristics of rapid data flow, massive data scale, dynamic data system, and various data types, and it has become increasingly apparent in improving innovation and entrepreneurship data analysis,...
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Big data has the characteristics of rapid data flow, massive data scale, dynamic data system, and various data types, and it has become increasingly apparent in improving innovation and entrepreneurship data analysis, trend prediction, and decision support. In this paper, the authors analyze the economic function data and entrepreneurship analysis based on machine learning. The support vector pair is very sensitive to the choice of parameters, and the parameters obtained using the genetic algorithm will greatly improve the accuracy of the model prediction. When using the genetic algorithm to find parameters, the cv method is used for verification. By applying big data technologies and platforms, it can provide strong data support to establish entrepreneurship education;integrate and integrate various types of innovation and entrepreneurship data, improve the quality of data *** the same time, through big datamining and analysis, accurately determine market demand hotspots and innovation and entrepreneurship trends, and promote scientific planning of innovation and entrepreneurship strategies. The research results show that this research model can be applied to actual projects in the future, and help investors better understand the changes of market economy.
Due to the increased development and applications of satellite communication, GPS equipment, video tracking and other communication technologies, the trajectory prediction of various SIs can be accurately predicted an...
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Due to the increased development and applications of satellite communication, GPS equipment, video tracking and other communication technologies, the trajectory prediction of various SIs can be accurately predicted and tracked. Based on these technologies, mobile targets are extensively used in many applications. A large amount of trajectory data predicted by trajectory can be collected from the positioning terminal of the moving target by the signal-receiving device. This paper analyzes the application of computer technology in trajectory prediction of moving objects in data mining algorithm and proposes a trajectory analysis method based on structure and cloud computing motion capture algorithm. The motion and trajectory characteristics of moving target trajectories are analyzed from microcosmic angle. By comparing the structural features of the extracted trajectory and the trajectory predicted by the trajectory of the moving object, the motion characteristics of the object can be analyzed more comprehensively. In addition, the sensitivity of the trajectory structure can be flexibly adjusted by setting the weight of the trajectory structure, so that the trajectory of the moving target can be analyzed and predicted in an effective and flexible way.
Association rule mining, studied for over ten years in the literature of datamining, aims to help enterprises with sophisticated decision making, but the resulting rules typically cannot be directly applied and requi...
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Association rule mining, studied for over ten years in the literature of datamining, aims to help enterprises with sophisticated decision making, but the resulting rules typically cannot be directly applied and require further processing. In this paper, we propose a method for actionable recommendations from itemset analysis and investigate an application of the concepts of association rules-maximal-profit item selection with cross-selling effect (MPIS). This problem is about choosing a subset of items which can give the maximal profit with the consideration of cross-selling effect. A simple approach to this problem is shown to be NP-hard. A new approach is proposed with consideration of the loss rule-a rule similar to the association rule-to model the cross-selling effect. We show that MPIS can be approximated by a quadratic programming problem. We also propose a greedy approach and a genetic algorithm to deal with this problem. Experiments are conducted, which show that our proposed approaches are highly effective and efficient.
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