Algorithms of Data Mining and Knowledge Discovery of Correlativity in Two-Dimensional Time Series

Abstract:

Oriented at dynamic data from complicated process with noise disturbance, it is very difficult to discover knowledge of correlativity and orderliness. Following some analyzing results about the shortcoming of relative coefficients in mining non-stationary time series, a series of new algorithms are built in this paper to mine correlativity in two-dimensional time series. These new algorithms are based on a expansible framework of model set. Based on these new mining algorithms, a making decision table is listed not only to mine correlativity in two-dimensional time series, but also to discover deepening knowledge to transform the qualitative knowledge “nonlinear relativity” as well as “non-relativity” into deeper quantitative knowledge about analytical model. These new approaches given in this paper is exoteric in framework and can be enriched with additional new models. In this way, some professional data mining and knowledge discovery cab be fulfilled to aim at some specific professional fields.

• Periodical: Applied Mechanics and Materials (Volumes 263-266)
• Pages: 1844-1848
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.263-266.1844
• Online since: December 2012
• Authors: Shao Lin Hu, Ye Li, Wei Zhang
• Keywords: Correlativity, Data Mining (DM), Knowledge Discovery, Time Series

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