Kuen-Fang Jea

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In an online data stream, the composition and distribution of the data may change over time, which is a phenomenon known as concept drift. The occurrence of concept drift can affect considerably the performance of a data stream mining method, especially in relation to mining accuracy. In this paper, we study the problem of mining frequent patterns from(More)
To speed up the task of association rule mining, a novel concept based on support approximation has been previously proposed for generating frequent itemsets. However, the mining technique utilized by this concept may incur unstable accuracy due to approximation error. To overcome this drawback, in this paper we combine a new clustering method with support(More)
A data stream is a massive and unbounded sequence of data elements that are continuously generated at a fast speed. Compared with traditional data mining, knowledge discovery in data streams is more challenging since several requirements need to be satisfied. In this paper we propose a mining algorithm for finding frequent itemsets over a transactional data(More)
In this paper, we deal with an NP-hard minimization problem, performing data allocation over multiple broadcast channels in the wireless environment. Our idea is to solve the discrete case of such a problem by the concept of gradient in the Euclidean space Rn . The theoretical basis of the novel idea ensures the near-optimality of our solution. Furthermore,(More)