It is difficult to visualize sets of data in space if the data variables are more than three. If the numbers of variables in the data set is very large, a lots of computation and lots of data plots are need.
In the analysis of weather data, we can use PCA, in which we can find variable which has most significant role for the weather change and the variables which has least role. For example which parameter plays major role for the precipitation either humidity or temperature or else. PCA could be the tool for the analysis.
Another application of PCA could be in the evaluation of scores in the examination as PCA considers not only data around which scores are centered but also considers how the scores are scattered. whereas classical methods of evaluation only considers the values around which data are centered.
Most recently searching engines are using PCA for the clustering of possible results found, dimension reduction is another use of PCA in data compression.
In the analysis of weather data, we can use PCA, in which we can find variable which has most significant role for the weather change and the variables which has least role. For example which parameter plays major role for the precipitation either humidity or temperature or else. PCA could be the tool for the analysis.
Another application of PCA could be in the evaluation of scores in the examination as PCA considers not only data around which scores are centered but also considers how the scores are scattered. whereas classical methods of evaluation only considers the values around which data are centered.
Most recently searching engines are using PCA for the clustering of possible results found, dimension reduction is another use of PCA in data compression.
