Implementation of time series data clustering based on SVD for stock data analysis on hadoop platform

With a growing amount of data, a viable solution is to use a cluster consisting of a large of computers for parallel processing, and Hadoop parallel computing platform is a typical representative. Clustering analysis for time series data is one of the main methods mining time series data, however, general clustering algorithms can´t perform clustering for time series data directly since series data has a special structure. The time series clustering algorithm presented is a combining algorithm from algorithms of Canopy and K-means based on SVD. Using singular value decomposition for feature extraction from the time series data, and then use Canopy and K-means algorithms to clustering analysis the feature data of the time series, at last, the algorithm is implemented on Hadoop platform by Mahout leading to a new clustering method that can handle massive time series data. Finally, this new clustering analysis method is successfully applied to real stock time series data with a satisfactory result.