Authors:
Leila Hamdad
1
;
Amine Abdaoui
2
;
Nabila Belattar
1
and
Mohamed Al Chikha
1
Affiliations:
1
ESI, Algeria
;
2
LIRMM, France
Keyword(s):
Spatial Data Mining, Geo-Visualization, Classification, Clustering, Association Rules.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Clustering and Classification Methods
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Software Development
;
Symbolic Systems
;
Visual Data Mining and Data Visualization
Abstract:
Spatial Data Mining allows users to extract implicit but valuable knowledge from spatial related data. Two
main approaches have been used in the literature. The first one applies simple Data Mining algorithms after a
spatial pre-processing step. While the second one consists of developing specific algorithms that considers
the spatial relations inside the mining process. In this work, we first present a study of existing Spatial Data
Mining tools according to the implemented tasks and specific characteristics. Then, we illustrate a new open
source Spatial Data Mining platform (EasySDM) that integrates both approaches (pre-processing and dynamic
mining). It proposes a set of algorithms belonging to clustering, classification and association rule mining
tasks. Moreover and more importantly, it allows geographic visualization of both the data and the results.
Either via an internal map display or using any external Geographic Information System.