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From Data Mining to Knowledge Discovery in Databases

Note that in actual kdd applications there are

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Unformatted text preview: resent persons who have defaulted on their loans and (2) the o’s represent persons whose loans are in good status with the bank. Thus, this simple artificial data set could represent a historical data set that can contain useful knowledge from the point of view of the bank making the loans. Note that in actual KDD applications, there are typically many more dimensions (as many as several hundreds) and many more data points (many thousands or even millions). FALL 1996 43 Articles Debt The purpose here is to illustrate basic ideas on a small problem in two-dimensional space. o No Loan o x o x x o x o o x x x o o x x o Loan o o x Data-Mining Methods o o Income Figure 3. A Simple Linear Classification Boundary for the Loan Data Set. The shaped region denotes class no loan. Debt o x x x x x o o x o o o o x x o o x io ess gr Re o x o o Income Figure 4. A Simple Linear Regression for the Loan Data Set. 44 AI MAGAZINE ine nL o The two high-level primary goals of data mining in practice tend to be prediction and description. As stated earlier, prediction involves using some variables or fields in the database to predict unknown or future values of other variables of interest, and description focuses on finding human-interpretable patterns describing the data. Although the boundaries between prediction and description are not sharp (some of the predictive models can be descriptive, to the degree that they are understandable, and vice versa), the distinction is useful for understanding the overall discovery goal. The relative importance of prediction and description for particular data-mining applications can vary considerably. The goals of prediction and description can be achieved using a variety of particular data-mining methods. C lassification is learning a function that maps (classifies) a data item into one of several predefined classes (Weiss and Kulikowski 1991; Hand 1981). Examples of classification methods used as part of knowledge discovery applications include the classifying of trends in financial markets (Apte and Hong 1996) and the automated identification of objects of interest in large image databases (Fayyad, Djorgovski, and Weir 1996). Figure 3 shows a simple partitioning of the loan data into two class regions; note that it is not possible to separate the classes perfectly using a linear decision boundary. The bank might want to use the classification regions to automatically decide whether future loan applicants will be given a loan or not. Regression is learning a function that maps a data item to a real-valued prediction variable. Regression applications are many, for example, predicting the amount of biomass present in a forest given remotely sensed microwave measurements, estimating the probability that a patient will survive given the results of a set of diagnostic tests, predicting consumer demand for a new product as a function of advertising expenditure, and predicting time series where the input variables can be time-lagged versions of the prediction variable. Figure 4 shows the result of simple linear regression where total debt is fitted as a linear function of income...
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