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From Data Mining to Knowledge Discovery in Databases

Given these notions we can consider a pattern to be

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Unformatted text preview: ovelty, usefulness, and simplicity. Interestingness functions can be defined explicitly or can be manifested implicitly through an ordering placed by the KDD system on the discovered patterns or models. Given these notions, we can consider a pattern to be knowledge if it exceeds some interestingness threshold, which is by no means an attempt to define knowledge in the philosophical or even the popular view. As a matter of fact, knowledge in this definition is purely user oriented and domain specific and is determined by whatever functions and thresholds the user chooses. Data mining is a step in the KDD process that consists of applying data analysis and discovery algorithms that, under acceptable computational efficiency limitations, produce a particular enumeration of patterns (or models) over the data. Note that the space of FALL 1996 41 Articles patterns is often infinite, and the enumeration of patterns involves some form of search in this space. Practical computational constraints place severe limits on the subspace that can be explored by a data-mining algorithm. The KDD process involves using the database along with any required selection, preprocessing, subsampling, and transformations of it; applying data-mining methods (algorithms) to enumerate patterns from it; and evaluating the products of data mining to identify the subset of the enumerated patterns deemed knowledge. The data-mining component of the KDD process is concerned with the algorithmic means by which patterns are extracted and enumerated from data. The overall KDD process (figure 1) includes the evaluation and possible interpretation of the mined patterns to determine which patterns can be considered new knowledge. The KDD process also includes all the additional steps described in the next section. The notion of an overall user-driven process is not unique to KDD: analogous proposals have been put forward both in statistics (Hand 1994) and in machine learning (Brodley and Smyth 1996). The KDD Process The KDD process is interactive and iterative, involving numerous steps with many decisions made by the user. Brachman and Anand (1996) give a practical view of the KDD process, emphasizing the interactive nature of the process. Here, we broadly outline some of its basic steps: First is developing an understanding of the application domain and the relevant prior knowledge and identifying the goal of the KDD process from the customer’s viewpoint. Second is creating a target data set: selecting a data set, or focusing on a subset of variables or data samples, on which discovery is to be performed. Third is data cleaning and preprocessing. Basic operations include removing noise if appropriate, collecting the necessary information to model or account for noise, deciding on strategies for handling missing data fields, and accounting for time-sequence information and known changes. Fourth is data reduction and projection: finding useful features to represent the data depending...
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