Independent Component Analysis (ICA) is a useful statistical method for separating mixed data sources into statistically independent patterns. In this paper, we apply ICA to transform multivariate time series data into independent components (ICs), and then propose a clustering algorithm called ICACLUS to group underlying data series according to the ICs found. This clustering algorithm can be used to identify stocks with similar stock price movement. The experiments show that this method is effective and efficient, which also outperforms other comparable clustering methods, such as K-means. Copyright © 2005 Springer-Verlag Berlin Heidelberg.
|Title of host publication||Advanced data mining and applications: First International Conference, ADMA 2005, Wuhan, China, July 22-24, 2005. proceedings|
|Editors||Xue LI, Shuliang WANG, Zhao Yang DONG|
|Place of Publication||Berlin|
|Publication status||Published - 2005|