Data classification is the process of determination of class intervals and class boundaries in the data. On the basis of class intervals and class boundaries the data is mapped and organized. There are four types of data classifications including Natural Breaks Classification, Quantile Classification, Equal Interval Classification and Standard Deviation Classification. I am explaining standard deviation classification and Quantile Classification.
In Standard deviation classification, the mean value is calculated then it places class breaks above and below the mean at intervals of either 0.25 or 0.5. In this method the values that are beyond the three standard deviations from the mean are categorized into two classes. In other words, greater than three standard deviation above the mean calculated and less than three standard deviation below the mean calculated will result the two classes of data. In quantile classification, set of values are distributed into groups that contain equal number of values. The same number of data values are placed in each class. This method is also commonly used for the classification of data.
In Standard deviation classification, the mean value is calculated then it places class breaks above and below the mean at intervals of either 0.25 or 0.5. In this method the values that are beyond the three standard deviations from the mean are categorized into two classes. In other words, greater than three standard deviation above the mean calculated and less than three standard deviation below the mean calculated will result the two classes of data. In quantile classification, set of values are distributed into groups that contain equal number of values. The same number of data values are placed in each class. This method is also commonly used for the classification of data.
