By Alder M.
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Extra info for An Introduction to Pattern Recognition
Html [12/12/2000 4:04:22 AM] Clustering: supervised v unsupervised learning Next: Dynamic Patterns Up: Basic Concepts Previous: CART et al Clustering: supervised v unsupervised learning The reflective reader will, perhaps, have been turning to the not so silly question of how he or she tells men from women. , if instead of having labelled one set as X points for males and O points for females, suppose we had just drawn unlabelled points as little black dots: could a program have looked at the data and seen that there are two populations?
In general, we have a set of objects which human beings have decided belong into a finite number of classes or categories, for example, the objects might be human beings, or letters of the alphabet. We have some choice of measuring process which is applied to each object to turn it into a point in some space, or alternatively a vector or array of numbers. (If the vectors all have length n we say they are n-dimensional: 2 and 3 dimensional vectors correspond in an obvious way to points in a plane and in the space we live in by simply setting up a co-ordinate system.
I say it would be essentially the same; well it would be very different for a human being to make sense of lots of columns of numbers, but a computer program hasn't got eyes. The computer program has to be an embodiment of a set of rules which operates on a collection of columns of numbers, and the length of the column is not likely to be particularly vital. Any algorithm which will solve the two class, two dimensional case, should also solve the k class n dimensional case, with only minor modifications.