Quantization of Continuous Data for Pattern Based Rule Extraction

A great deal of interesting real-world data is encountered through the analysis of continuous variables, however many of the robust tools for rule discovery and data characterization depend upon the underlying data existing in an ordinal, enumerable or discrete data domain. Tools that fall into this category include much of the current work in fuzzy logic and rough sets, as well as all forms of event-based pattern discovery tools based on probabilistic inference. Through the application of discretization techniques, continuous data is made accessible to the analysis provided by the strong tools of discrete-valued data mining. The most common approach for discretization is quantization, in which the range of observed continuous valued data are assigned to a fixed number of quanta, each of which covers a particular portion of the range within the bounds provided by the most extreme points observed within the continuous domain. This chapter explores the effects such quantization may have, and the techniques that are available to ameliorate the negative effects of these efforts, notably fuzzy systems and rough sets.