Potential Usage Estimation of Ground Water using Spatial Association Rule Mining

Abstract

Abstract

Spatial association discovery process using frequent subgraph mining

“…A rough-fuzzy set based rule extraction model is used to deal with both fuzziness and roughness; [10] combines and extend techniques developed in both spatial and fuzzy data mining to deal with the uncertainty found in typical spatial data. This proposal uses fuzzy logic to get relevant information from transition areas between spatial neighborhoods to spatial association mining and for spatial relationships modelling; [11,12] propose an algorithm for local patterns discovery considering spatial heterogeneity that incorporates a novel spatial metric for support evaluation based on event density in a particular area; [13] presents a specially designed algorithm to discover spatial associations related to El Niño Southern Oscillation (ENSO); [14] applies an algorithm that explores multiple spatial objects hierarchies; [15] uses A-Priori-based approaches to find spatial association rules; [6,16] propose using Inductive Logic Programming (ILP) for reach this data mining purpose by modelling and stracting high support spatial relations from spatial data. [17] worked with metaheuristics such as genetic algorithms and evolutionary programming; [18] suggested a data-transformation approach before using traditional association rule mining algorithms; [19] introduced non-trivial structures such as graphs for spatial relationship representation; among others.…”