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MLDM
2009
Springer

Regional Pattern Discovery in Geo-referenced Datasets Using PCA

13 years 10 months ago
Regional Pattern Discovery in Geo-referenced Datasets Using PCA
Existing data mining techniques mostly focus on finding global patterns and lack the ability to systematically discover regional patterns. Most relationships in spatial datasets are regional; therefore there is a great need to extract regional knowledge from spatial datasets. This paper proposes a novel framework to discover interesting regions characterized by “strong regional correlation relationships” between attributes, and methods to analyze differences and similarities between regions. The framework employs a twophase approach: it first discovers regions by employing clustering algorithms that maximize a PCA-based fitness function and then applies post processing techniques to explain underlying regional structures and correlation patterns. Additionally, a new similarity measure that assesses the structural similarity of regions based on correlation sets is introduced. We evaluate our framework in a case study which centers on finding correlations between arsenic pollution an...
Oner Ulvi Celepcikay, Christoph F. Eick, Carlos Or
Added 27 May 2010
Updated 27 May 2010
Type Conference
Year 2009
Where MLDM
Authors Oner Ulvi Celepcikay, Christoph F. Eick, Carlos Ordonez
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