In this paper, new approaches for chaotic time series prediction areintroduced. We first summarize and evaluate the existing local prediction models, then proposeoptimization models and new algorithms to modify proced...In this paper, new approaches for chaotic time series prediction areintroduced. We first summarize and evaluate the existing local prediction models, then proposeoptimization models and new algorithms to modify procedures of local approximations. Themodification to the choice of sample sets is given, and the zeroth-order approximation is improvedby a linear programming method. Four procedures of first-order approximation are compared, andcorresponding modified methods are given. Lastly, the idea of nonlinear feedback to raise predictingaccuracy is put forward. In the end, we discuss two important examples, i.e. Lorenz system andRoessler system, and the simulation experiments indicate that the modified algorithms are effective.展开更多
With the development of Internet, frequent pattern mining has been extendedto more complex patterns like tree mining and graph mining. Such applications arise in complexdomains like bioinformatics, web mining, etc. In...With the development of Internet, frequent pattern mining has been extendedto more complex patterns like tree mining and graph mining. Such applications arise in complexdomains like bioinformatics, web mining, etc. In this paper, we present a novel algorithm, namedChopper, to discover frequent subtrees from ordered labeled trees. An extensive performance studyshows that the newly developed algorithm outperforms TreeMiner V, one of the fastest methodsproposed previously, in mining large databases. At the end of this paper, the potential improvementof Chopper is mentioned.展开更多
Mining frequent patterns from datasets is one of the key success of data mining research. Currently, most of the studies focus on the data sets in which the elements are independent, such as the items in the marketing...Mining frequent patterns from datasets is one of the key success of data mining research. Currently, most of the studies focus on the data sets in which the elements are independent, such as the items in the marketing basket. However, the objects in the real world often have close relationship with each other. How to extract frequent patterns from these relations is the objective of this paper. The authors use graphs to model the relations, and select a simple type for analysis. Combining the graph theory and algorithms to generate frequent patterns, a new algorithm called Topology, which can mine these graphs efficiently, has been proposed. The performance of the algorithm is evaluated by doing experiments with synthetic datasets and real data. The experimental results show that Topology can do the job well. At the end of this paper, the potential improvement is mentioned.展开更多
文摘In this paper, new approaches for chaotic time series prediction areintroduced. We first summarize and evaluate the existing local prediction models, then proposeoptimization models and new algorithms to modify procedures of local approximations. Themodification to the choice of sample sets is given, and the zeroth-order approximation is improvedby a linear programming method. Four procedures of first-order approximation are compared, andcorresponding modified methods are given. Lastly, the idea of nonlinear feedback to raise predictingaccuracy is put forward. In the end, we discuss two important examples, i.e. Lorenz system andRoessler system, and the simulation experiments indicate that the modified algorithms are effective.
文摘With the development of Internet, frequent pattern mining has been extendedto more complex patterns like tree mining and graph mining. Such applications arise in complexdomains like bioinformatics, web mining, etc. In this paper, we present a novel algorithm, namedChopper, to discover frequent subtrees from ordered labeled trees. An extensive performance studyshows that the newly developed algorithm outperforms TreeMiner V, one of the fastest methodsproposed previously, in mining large databases. At the end of this paper, the potential improvementof Chopper is mentioned.
文摘Mining frequent patterns from datasets is one of the key success of data mining research. Currently, most of the studies focus on the data sets in which the elements are independent, such as the items in the marketing basket. However, the objects in the real world often have close relationship with each other. How to extract frequent patterns from these relations is the objective of this paper. The authors use graphs to model the relations, and select a simple type for analysis. Combining the graph theory and algorithms to generate frequent patterns, a new algorithm called Topology, which can mine these graphs efficiently, has been proposed. The performance of the algorithm is evaluated by doing experiments with synthetic datasets and real data. The experimental results show that Topology can do the job well. At the end of this paper, the potential improvement is mentioned.
