Supervised vs Unsupervised (Perspective: Maximum Likelihood to Select The Sample Area)
As we known, data mining techniques come in two main forms: supervised (also known as predictive or directed) and unsupervised (also known as descriptive or un-directed). Both categories encompass functions capable of finding different hidden patterns in large data sets. Supervised data mining techniques are appropriate when we have a specific target value that you’d like to predict about your data. The targets can have two or more possible outcomes, or even be a continuous numeric value (more on that later). The accuracy is determined by the quality of the sampling and the number of samples. The sample area is created using Region Of Interest (ROI) . ROI must first be created before conducting this supervised classification process. Region Of Interest is the sampling area formed as a training area on the supervised classification. The classification model can have more than two possible values in the target attribute. To use these methods, we ideally have a subset of data poin