Mining Geospatial Data In A Transductive Setting
Author(s)
A. Appice, N. Barile, M. Ceci, D. Malerba & R. P. Singh
Abstract
Many organizations collect large amounts of spatially referenced data. Spatial
Data Mining targets the discovery of interesting, implicit knowledge from such
data. The specific classification task has been extensively investigated in the
classical inductive setting, where only labeled examples are used to generate a
classifier, discarding a large amount of information potentially conveyed by the
unlabeled instances to be classified. In this work spatial classification is based
on transduction, an inference mechanism \“from particular to particular” which
uses both labeled and unlabeled data to build a classifier whose main goal is
that of classifying (only) unlabeled data as accurately as possible. The proposed
method, named TRANSC, employs a principled probabilistic classification in
multi-relational data mining to face the challenges posed by handling spatial data.
The predictive accuracy of TRANSC has been evaluated on two real-world spatial
datasets.
1 Introduction
The expanding market for spatial databases and Geographic Information System
(GIS) technologies is driven by the pressure from the public sector, environmental
agencies and industries to provide innovative solutions to data applications that
involve spatial data, that is, a collection of (spatial) objects organized in thematic
layers (e.g., roads, rivers). A thematic layer is characterized by a geometrical
representation as well as several non-spatial attributes, called thematic attributes.
A GIS provides the set of functionalities to store, retrieve and manage both
geometrical representation and thematic attributes stored in a spatial database.
Anyway, the range of GIS applications can be extended by adding spatial
data mining facilities to the systems [1] to extract implicit knowledge from
georeferenced data.
Keywords
Related Book
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