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In your message there are several unfamiliar abbreviatures for me, that should be deciphered:
Concerning PCA, there is enormous amount of links in Internet (use google.com). As usual, PCA is applied for problems of attributes space dimension reduction. It means, that if we have too many attributes in the dataset we can select several of them (most often, 2 or 3), which are the most informative according to the dispersion criterion. In general, 1st PC contains approximately 95% of information or more. Also PCA is used in order to plot multidimensional time-series and to discover isolated groups in data's structure (so-called clusters).