A Discovery Method of Trend Rules from Complex Sequential Data

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This paper proposes a method that discovers trend rules from complex sequential data. The rules represent relationships among evaluation objects, keywords, and changes of numerical values related to the evaluation objects. The data is composed of numerical sequential data and text sequential data. The method extracts frequent patterns from transaction sets based on the changes. Also, it regards combinations of the patterns and the changes as trend rules. This paper applies the method to data sets composed of stock data and news headlines. Lastly, this paper compares the method with a method based on the random selection and shows the effect of the proposed method.

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