ACM Computing Surveys (CSUR)
Communications of the ACM
Using Multivariate Clustering to Characterize Ecoregion Borders
Computing in Science and Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
Discovery of climate indices using clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
An exploration of climate data using complex networks
ACM SIGKDD Explorations Newsletter
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Various clustering methods have been applied to climate, ecological, and other environmental datasets, for example to define climate zones, automate land-use classification, and similar tasks. Measuring the "goodness" of such clusters is generally application-dependent and highly subjective, often requiring domain expertise and/or validation with field data (which can be costly or even impossible to acquire). Here we focus on one particular task: the extraction of ocean climate indices from observed climatological data. In this case, it is possible to quantify the relative performance of different methods. Specifically, we propose to extract indices with complex networks constructed from climate data, which have been shown to effectively capture the dynamical behavior of the global climate system, and compare their predictive power to candidate indices obtained using other popular clustering methods. Our results demonstrate that network-based clusters are statistically significantly better predictors of land climate than any other clustering method, which could lead to a deeper understanding of climate processes and complement physics-based climate models.