DATA CHARACTERIZATION TOWARDS MODELING FREQUENT PATTERN MINING ALGORITHMS

Big data quickly comes under the spotlight in recent years. As big data is supposed to handle extremely huge amount of data, it is quite natural that the demand for the computational environment to accelerates, and scales out big data applications increases. The important thing is, however, the behavior of big data applications is not clearly defined yet. Among big data applications, this paper specifically focuses on stream mining applications. The behavior of stream mining applications varies according to the characteristics of the input data. The parameters for data characterization are, however, not clearly defined yet, and there is no study investigating explicit relationships between the input data, and stream mining applications, either. Therefore, this paper picks up frequent pattern mining as one of the representative stream mining applications, and interprets the relationships between the characteristics of the input data, and behaviors of signature algorithms for frequent pattern mining.

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