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Introduction
Data mining has been defined as the search for useful and previously unknown patterns in large datasets. Yet when faced with the task of mining a large dataset, it is not always obvious where to start and how to proceed. The purpose of this book is to introduce a methodology for data mining and to guide you in the application of that methodology using software specifically designed to support the methodology. In this chapter, we provide an overview of the methodology. The chapters that follow add detail to that methodology and contain a sequence of exercises that guide you in its application. The exercises use VisMiner, a powerful visual data mining tool which was designed around the methodology.
Data Mining Objectives
Normally in data mining a mathematical model is constructed for the purpose of prediction or description. A model can be thought of as a virtual box that accepts a set of inputs, then uses that input to generate output.
Prediction modeling algorithms use selected input attributes and a single selected output attribute from your dataset to build a model. The model, once built, is used to predict an output value based on input attribute values. The dataset used to build the model is assumed to contain historical data from past events in which the values of both the input and output attributes are known. The data mining methodology uses those values to construct a model that best fits the data. The process of model construction is sometimes referred ...
