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A fast bidirectional method for mining maximal frequent itemsets

A fast bidirectional method for mining maximal frequent item...

3rd International Joint Conference on Computational Sciences and optimization, CSO 2010: Theoretical Development and Engineering Practice

作者： Wang, Chao Ni, Zhi-Wei Guo, Jun-Fen Key Laboratory of Process Optimization and Intelligent Decision-making Hefei University of Technology Ministry of Education Heifei Hefei Anhui China

ISBN: (纸本)9780769540306

In this paper, a fast bidirectional method and an efficient data compression method for mining maximal frequent itemsets is proposed. A flexible search method is given, which exploits the advantages of bottom-up and up-bottom strategies. The compression technique use the Prime number characteristics to transform transaction data into a positive integer and can efficiently reduce the size of transaction database. This method can mine maximal frequent itemsets according to different user-defined mininum support with only one scan of original database. Theoretical and experimental analysis shows that the proposed method is scalable and efficient for mining maximal frequent itemsets. © 2010 IEEE.

关键词： Data compression

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The Pricing Strategy of Software: Vulnerability and Patch 20

The Pricing Strategy of Software: Vulnerability and Patch

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4th International Conference on Computer Science and Application Engineering, CSAE 2020

作者： Yang, Wujuan Qian, Xiaofei School of Management and Key Laboratory of Process Optimization and Intelligent Decision-making of the Ministry of Education Hefei University of Technology Hefei China

ISBN: (纸本)9781450377720

The ubiquity of vulnerability in software products poses a serious threat to network security and user system. Patching for the software is a common tool to ease the security issue. Whereas, not all users will choose to patch due to the consideration on compatibility and inconvenience. Thus, it is significant to induce users to patch for the software. Based on the background of this practical problem, this paper proposes two models (a benchmark model and a price discount model) to analyze the user's buying and patching behavior, the pricing strategy of the software vendor, and the vendor's benefits. By comparing the results of the benchmark model and the price discount model, we find that the price discount policy will result in a higher group security level and more benefits for the vendor. © 2020 ACM.

关键词： Game theory

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Extreme Risk Measurement of Carbon Market Considering Multifractal Characteristics

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Journal of Systems Science & Complexity 2023年第6期36卷 2497-2514页

作者： ZHU Dandan ZHANG Chen PAN Di School of Management Hefei University of TechnologyHefei230009China School of Economics and Management Shanghai University of Political Science and LawShanghai201701China Key Laboratory of Process Optimization and Intelligent Decision-Making Hefei University of TechnologyHefei230009China

Influenced by the global economy,politics,energy and other factors,the price of carbon market fluctuates *** is of great practical significance to explore a suitable measurement method of extreme risk of carbon *** that the return series of carbon market has the characteristics of leptokurtosis,fat tail,skewness and multifractal,and there maybe many extreme risk values in the carbon market,this paper introduces the Skewed-t distribution which can describe the characteristics of leptokurtosis,fat tail and skewness of return series into MSM model which can describe multifractal characteristic of return series to model volatility of carbon *** the basis,based on the extreme value theory,this paper constructs Skewed-t-MSM-EVT model to measure extreme risk of carbon *** paper chooses EUA market as the object to study extreme risk of carbon market,and draws the following conclusions:Skewed-t-MSM-EVT model has significantly higher prediction accuracy for carbon market's Va R than MSM-EVT models under other distributions(including normal distribution,t distribution,GED distribution);Skewed-t-MSM-EVT model is superior to traditional Skewed-t-FIGARCH-EVT and Skewed-t-GARCH-EVT models in predicting carbon market's Va *** research has important practical significance for accurately grasping the risk of carbon market and promoting energy conservation and emission reduction.

关键词： Carbon market extreme value theory multifractal Skewed-t distribution VaR

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A data-driven open decision framework based on adaptive evidential reasoning rule

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Computers & Industrial Engineering 2025年 206卷

作者： Chao Fu Wenjun Chang Min Xue Guangyan Lu School of Management Hefei University of Technology Hefei Box 270 Hefei 230009 Anhui China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei 230009 Anhui China Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies Hefei 230009 Anhui China Department of Operation Management CRRC Qishuyan Instiute Co. Ltd Changzhou Jiangsu 213011 China

Data accumulation is beneficial to efficiently make decisions on new cases. However, it is rare to use the historical data of experienced decision makers to help inexperienced decision makers generate solutions to new cases in an open and adaptive way. To address this issue, a data-driven open decision framework is developed based on an adaptive evidential reasoning (ER) rule. Giving two assumptions, criterion weights are learned from historical individual and overall assessments while criterion reliabilities are learned from historical individual assessments and gold standards. The adaptive ER rule is developed based on two abstract functions with the variables of criterion weight and criterion reliability, respectively, whose properties are analyzed. With these foundations, the general situation of the open decision framework is demonstrated from the learning, aggregation, and prediction hierarchies. Three special situations of the framework are presented to make it flexible. The historical data of more experienced decision makers are allowed in the framework to ensure its openness. The applicability of the general situation of the open decision framework is examined by its use in helping diagnose breast lesions. Its performance is highlighted by the comparison with the three special situations of the framework, five representative machine learning algorithms, and the framework with four forms of two abstract functions.

关键词： Learning algorithms

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Exploring the impact of COVID-19 on travel behavior based on EV charging big data 5

Exploring the impact of COVID-19 on travel behavior based on...

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5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021

作者： Zhou, Kaile Hu, Dingding Li, Fangyi Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education School of Management Hefei University of Technology Hefei230009 China

ISBN: (纸本)9781665434256

The COVID-19 pandemic has forced many governments around the world to implement strict lockdown measures and order citizens to stay at home, which has caused a major change in travel patterns. This study leveraged electric vehicle charging big data in Hefei, Anhui Province, China to estimate electric vehicle charging demand in the absence of the COVID-19 pandemic using multi-layer perceptron model, which quantified the impact of the COVID-19 pandemic. In addition, we employed the vector autoregressive model to investigate the dynamic relationships between the changes in charging demand and various explanatory factors. The results suggest that the daily average charging demand in Hefei decreased by 78.3% compared to the predicted value during the pandemic. Furthermore, according to the variance decomposition and impulse response function analysis, national confirmed COVID-19 cases play a dominant role in reducing charging demand. The number of daily hospitalizations and Migration Scale Index also have significant and robust effect on the decrease in charging demand. The Air Quality Index and Baidu Index are susceptible to external factors and do not have a direct impact on the change in charging demand. Findings support a better understanding of changes in travel behavior during the pandemic and provide policy makers with references to deal with similar events. © 2021 IEEE

关键词： COVID-19

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Competition in durable goods markets with product failures into consideration 9

Competition in durable goods markets with product failures i...

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9th International Symposium on Project Management, ISPM 2021

作者： Tian, Jianfei Qian, Xiaofei School of management Hefei University of Technology Anhui China Key Laboratory of Process Optimization and Intelligent Decision-making of the Ministry of Education China

ISBN: (纸本)9781921712777

Few studies on competition in dual channel for durable goods take the product failures into consideration. However, the factors related to preventive maintenance (PM) and minimal maintenance (MM) have an important impact on the optimal profits of the manufacturer and retailer. Consequently, we develop three game models (Single Selling channel (SS), Manufacturer Stackelberg (MS) and Cooperation Model (CM)) to study the players’ equilibrium solutions and analyze the impact of the initial point, numbers and level of preventive maintenance on equilibrium price and profits. We find that the equilibrium wholesale and retail price under SS tend to increase when the manufacturer decides to raise the repair charge, while in MS the manufacturer and retailer will select a higher level for the wholesale price, retail price and rental charge when manufacturer chooses to put off the initial point for PM. In addition, numerical examples are provided to supplement our conclusions. The results show that the manufacturer in MS will set the numbers and level of PM to the maximum threshold in order to maximize its profit. © ISPM *** right reserved.

关键词： Preventive maintenance

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Based IGARCH error correction of the PLS-SVR short-term load forecasting

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2012 International Conference on Electrical and Electronics Engineering, ICEE 2012

作者： Chen, Zhiqiang Yang, Shanlin Hou, Liqiang Key Lab of Process Optimization and Intelligent Decision-Making Hefei University of Technology Hefei Anhui 230009 China

ISBN: (纸本)9781461449805

Due to the complexity in the influencing factors of the prediction accuracy, using single forecasting method to improve the prediction accuracy is just impossible in practice. In this chapter, the partial least square (PLS)method was used to diminish the sample input data, which can improve the traditional Support Vector Regression (SVR) for short-time electricity load. Then, there is error sequence between the predictive value and the actual value, and the error sequence was considered as the forecasting data, which has the characteristics of obvious peak and fat tail. Next, Integrated Generalized Autoregressive Conditional Heteroskedasticity (IGARCH) model was used to build the electricity load error predicted model, and modify the original predictive value. Lastly, the forecasting method of this chapter based on PJM historical data was verified. The result shows that the mean absolute percentage error (MAPE) and mean square prediction error (MSPE) are 3.56 % and 1.75 %, respectively. Compared to other traditional predictive value, the model presented in this chapter has higher accuracy, which can be applied to predict the short-term electricity load. © 2014 Springer Science+Business Media New York.

关键词： Error correction

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Joint optimization of Production and Shipping for Small Orders Considering Less-Than-Container Loading 4th

Joint Optimization of Production and Shipping for Small Orde...

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4th International Conference on Resources and Environmental Research, ICRER 2022

作者： Tian, Yajing Wang, Guoqiang Chen, Yingying Luo, He School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization & Intelligent Decision-Making Ministry of Education Hefei China

ISBN: (纸本)9783031318078

This paper introduces a joint optimization problem of production and shipping for overseas small orders considering less-than-container loading (LCL) in manufacturing enterprises. The problem consists of finding a production schedule together with a LCL shipping scheme simultaneously to minimize the total cost. An integer programming model is developed, and the genetic algorithm of enhanced elite reservation (SEGA) is used to solve large-sized instances. Besides, the performance of SEGA is compared with the classical genetic algorithm (SGA) and stallion genetic algorithm (stud GA). It is statistically shown that SEGA outperforms other algorithms in finding near-optimal solutions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Loading

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A hybrid ant colony algorithm for the Cutting Stock Problem

A hybrid ant colony algorithm for the Cutting Stock Problem

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2010 International Conference on Future Information Technology and Management Engineering, FITME 2010

作者： Peng, Jin Chu, Zhang Shu School of Management Hefei University of Technology Key Laboratory Process Optimization and Intelligent Decision-Making Ministry of Education Hefei Anhui Province China

ISBN: (纸本)9781424490882

This study presents a new hybrid ant colony algorithm (HACO) to solve an in integer linear programming formulation of the Cutting Stock Problem(CSP). The CSP is an important class combinatorial problem. It is appropriate to minimize the raw material used by industries for fulfilling customer's demands. In such cases, classic models for solving the cutting stock problem are useless. HACO consists of GDCP procedure and modified ACO (MACO). GDCP procedure provides dominative cutting patterns as MACO solution components, in MACO the pheromone trail is put on cutting patterns instead of items. MACO preserves cutting patterns and frequencies information under ants building solution procedure. Results obtained from computational experiments for ten benchmarks demonstrate that the performance of MACO is compared to that obtained using existing mete-heuristic algorithm. © 2010 IEEE.

关键词： Heuristic algorithms

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Data stream closed frequent itemsets mining in blend window

Data stream closed frequent itemsets mining in blend window

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2012 International Conference on Computer Science and Service System, CSSS 2012

作者： Wu, Hao Wang, Huiying Li, Huaiying Jiang, Miao School of Management Hefei University of Technology India Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei 230009 China

ISBN: (纸本)9780769547190

In data stream mining, sliding window can record the latest and most useful patterns, but the best size can not be accurately determined. To aim at data with the characteristics of data flow in some simulation systems, this paper proposes a method for mining the closed frequent patterns in the mixed window of data stream. The pattern of data stream could be completely recorded by scanning the stream only once. And the pruning method of T-Moment could reduce the space complexity of sliding window tree and the maintenance cost of the closed frequent patterns tree. To differentiate the historical and the latest patterns, a time decaying model was applied. The experimental results show that the algorithm has good efficiency and accuracy. © 2012 IEEE.

关键词： Forestry

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