Difference between classification and segmentation in data mining tasks?
Can someone say what is difference between classification and segmentation in data mining tasks? Can we say that segmentation is classification task when objects are costumers and dividing criterias are relevant to Marketing? So segmentation is just sub-group of classification?
Segmentation methods divide a unit (be it text unit, an image, or other data structure) into smaller groups of connected sub-units, such as words, pixels, etc. Each segmented group has in the resulting raster (aka partition). That is, pixels belonging to that group will all have that particular ID Number (e.g., ID = 67897), but no other pixels outside that group will have that ID. In contrast, classification methods assign a class to each element, be it individual pixels or segments. Given a per-pixel classification, there will be groups of connected pixels sharing the same class, but pixels belonging to separate groups will have the same ID in the classified raster (e.g, ID = 8). There are labeling algorithms that can assign a unique ID to each group, so you can derive a segmentation (aka partition) from a classification, but you cannot derive a classification from a segmentation, for you don't know (yet) what the different segments have in common (i.e., you have an ID, but you don't have a class).
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