Data Mining: Practical Machine Learning Tools and ... - LIDeCC

chapter 3Output:Knowledge RepresentationMost of the techniques in this book produce easily comprehensible descriptionsof the structural patterns in the data. Before looking at how these techniqueswork, we have to see how structural patterns can be expressed. There are manydifferent ways for representing the patterns that can be discovered by machinelearning, and each one dictates the kind of technique that can be used to inferthat output structure from data. Once you understand how the output isrepresented, you have come a long way toward understanding how it can begenerated.We saw many examples of data mining in Chapter 1. In these cases the outputtook the form of decision trees and classification rules, which are basic knowledgerepresentation styles that many machine learning methods use. Knowledgeis really too imposing a word for a decision tree or a collection of rules, and byusing it we don’t really mean to imply that these structures vie with the real kindof knowledge that we carry in our heads: it’s just that we need some word torefer to the structures that learning methods produce. There are more complexvarieties of rules that allow exceptions to be specified, and ones that can express61