Application Research of a New Symbolic Approximation Method-SAX in Time Series Mining
Yi Liu, Depei Bao, Yang Ze-hong, Yannan Zhao, Jia Pei-fa, Jiaqin Wang · Computer Engineering and Applications Journal · 2006
Clustering is one of the most common data mining methods,being a hot topic in its own right as an exploratory tool,and also a subroutine in more complex algorithms such as rule discovery and abnormal discovery.As a typical time series data,stock data has been widely used in data mining research.In this paper,a new symbolic method-SAX[1] is used in stock data clustering analysis.We apply the new method on stock data which is obtained from the Standard Poor 500 index.Moreover,we use two similarity measure method including Euclidean and Dynamic Time Warping in our experiments.The experiments result shows that clustering can better focus on the whole trend and the efficiency can be improved with the help of SAX.