Data Discovery Approaches for Vague Spatial Data

Introduction

Data mining or knowledge discovery generally refers to a variety of techniques that have developed in the fields of databases, machine learning (Alpaydin, 2004) and pattern recognition (Han & Kamber, 2006). The intent is to uncover useful patterns and associations from large databases. For complex data such as that found in spatial databases (Shekar & Chawla, 2003) the problem of data discovery is more involved (Lu et al., 1993; Miller & Han, 2009).

Spatial data has traditionally been the domain of geography with various forms of maps as the standard representation. With the advent of computerization of maps, geographic information systems (GIS) have come to fore with spatial databases storing the underlying point, line and area structures needed to support GIS (Longley et al., 2001). A major difference between data mining in ordinary relational databases (Elmasri & Navathe, 2010) and in spatial databases is that attributes of the neighbors of some object of interest may have an influence on the object and therefore have to be considered as well. The explicit location and extension of spatial objects define implicit relations of spatial neighborhood (such as topological, distance and direction relations), which are used by spatial data mining algorithms (Ester et al 2000).

Additionally when wish to consider vagueness or uncertainty in the spatial data mining process (Burrough & Frank 1996; Zhang & Goodchild, 2002), an additional level of difficulty is added. In this chapter we describe one of the most common data mining approaches, discovery of association rules, for spatial data for which we consider uncertainty in the extraction rules as represented by both fuzzy set and rough set techniques.