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Data Mining in non-stationary data streams is gaining more attentionrecently, especially in the context of Internet of Things and Big Data. It is a highly challenging task, since the fundamentally different typesof possibly occurring drift undermine classical assumptions such asi.i.d. data or stationary distributions. Available algorithms are either struggling with certain forms of drift or require...
Today, scientific and business applications generate huge amounts of data. Users of data grid, who are distributed all over the grid geographically, need such data. So ensuring the access to this distributed data efficiently is one of the most important challenges in Data grid network. Data replication algorithms are known as the most common method used to overcome this problem. They distribute several...
This paper proposes a novel optimization scheme by hybridizing an artificial bee colony optimizer (HABC) with a bee life-cycle mechanism, for both stationary and dynamic optimization problems. The main innovation of the proposed HABC is to develop a cooperative and population-varying scheme, in which individuals can dynamically shift their states of birth, foraging, death, and reproduction throughout...
In order to realize the objective and synthetic evaluation of the feasibility in Low-carbon (LC) project, the paper constructs the knowledge representation system(i.e. attribute value of information system), applies the reduction and the mining rules of the Rough Set Theory, at same the time computing dynamic weight, subjective weight, objective weight are combined with Analytic Hierarchy Process...
Clustering is a method of unsupervised learning, and a common technique for statistical data analysis used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics a novel algorithm based on clustering to extract rules from neural networks is proposed. After neural networks have been trained and pruned successfully, inner-rules are generated by...
With the development of Internet, more and more firms adopt dynamic pricing as a valid method to maximize their profit, especially, when the demand is uncertainty. But on the other hand, the consumers become cleverer than before. Customers behave strategically and weigh their payoff of immediate purchase against the expected payoff of delaying their purchases. In this paper, we use a dynamic pricing...
Biological networks having complex connectivity have been widely studied recently. By characterizing their inherent and structural behaviors in a topological perspective, these studies have attempted to discover hidden knowledge in the systems. However, even though various algorithms with graph-theoretical modeling have provided fundamentals in the network analysis, the availability of practical approaches...
Circular economy, as an advanced development mode for harmonizing the Problems among resource, environment and economy, is destined to be the choice for coal concerned enterprises to explore new industrial way and keep on developing. The paper eliminated the redundant data in index membership for object classification by defining distinguishable weight and extracted valid values to compute object...
The cellular genetic algorithm with disaster (CDGA) puts individuals into a toridal grid or presudo landscape which behave "active" or "inactive" state. The genetic operator of individuals is restricted to within neighborhood. In this paper, we evaluate combinational effects on size of neighborhood and size of disaster. We have tackled this research by considering two typical functions...
Due to the high fatality rate of patients with radiation pneumonitis (RP), a complication of the radiation therapy (radiotherapy), great attention has been paid to the treatment plan of individual RP patients. Therefore, not only technological advances in the development of treatment planning systems but also new prognostic models are urgently required to lessen the complication and to predict the...
Network reconstruction, i.e., obtaining network structure from data, is a central theme in systems biology, economics, and engineering. Previous work introduced dynamical structure functions as a tool for posing and solving the problem of network reconstruction between measured states. While recovering the network structure between hidden states is not possible since they are not measured, in many...
In this paper, we use a Least Squares Temporal Difference (LSTD) algorithm in an actor-critic framework where the actor and the critic operate concurrently. That is, instead of learning the value function or policy gradient of a fixed policy, the critic carries out its learning on one sample path while the policy is slowly varying. Convergence of such a process has previously been proven for the first...
In this paper, a dynamic pricing model for e-commerce based on data mining is proposed after the comprehensive analysis of data mining technology applications and e-commerce dynamic pricing strategies. The authors introduce this model into the pricing mechanisms of TaoBao, and discuss the application of the model in C2C and B2C modes, which has great reference value for e-commerce enterprise operation.
A cellular automata model (CAM) is presented to simulate the passenger evacuation of carriage in an emergency. Some special algorithm is introduced considering simple human judgment to make the rules more reasonable. Based on the above model, a program CARUN is developed. Considering the special geometry and boundary conditions, the simulation of the evacuation process for the passenger in carriage...
Existing density-based data stream clustering algorithms use a two-phase scheme approach consisting of an online phase, in which raw data is processed to gather summary statistics, and an offline phase that generates the clusters by using the summary data. In this paper we propose a data stream clustering method based on a multi-agent system that uses a decentralized bottom-up self-organizing strategy...
Considering the uncertainty of information, the paper puts forward an improved grey dynamic programming model. After that, treating profit value as positive interval grey number, the paper researches on dynamic programming model, and we could get the optimal strategy after solution by means of defining standard interval grey number. Furthermore, as we can not make the judgment of standard interval...
Grid system is inclined to support resource sharing for mixed types of workloads nowadays to improve the whole user utility, which requires an effective allocation method that not only each workload user can have desirable resources to satisfy its performance requirement from the view of user, but also can avoid shortage or waste of resources from the view of system. Currently, most resource allocation...
A novel dynamic evolutionary clustering algorithm (DECA) is proposed in this paper to overcome the shortcomings of fuzzy modeling method based on general clustering algorithms that fuzzy rule number should be determined beforehand. DECA searches for the optimal cluster number by using the improved genetic techniques to optimize string lengths of chromosomes; at the same time, the convergence of clustering...
In this paper, we simultaneously study the uncapacitated fixed-charge location problem (UFLP) and a multi-echelon inventory problem. We initially develop an approximate integer programming formulation for the inventory problem. The later problem is combined with the UFLP and the integrated model is presented as a 0-1 integer program that we refer to as the joint facility location inventory problem...
It has been shown that the use of a reciprocation mechanism in peer-to-peer grid systems which provide multiple services to their users is an efficient way to prevent free-riding and, at the same time, to promote the clustering of peers that have mutually profitable interactions. However, when peers are subject to resource limitations, they may be unable to offer all possible services and shall select...
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