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A new PCA algorithm is introduced, utilizing a rough cluster-based granulation scheme for segmentation of multivariate time series and process monitoring purposes. This granulated cluster-based algorithm can be used for segmentation of multivariate time series and initialization of other partitioning clustering methods that need to have good initialization parameters. The proposed algorithm is suitable for mining data sets, which are large both in dimension and size, in case generation. It utilizes Principal Component Analysis (PCA) specification and an innovative granular computing method for detection of changes in the hidden structure of multivariate time series data in a bottom up cluster merging manner. Rough set theory is used for feature extraction and solving superfluous attributes issue.
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