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In real world, social networks are large scale, noisy and evolutionary. Communities are inherent characteristics of human interaction in social networks. Tracking evolutionary communities in dynamic social networks has become an increasingly important research topic. Several classic incremental clustering and evolutionary clustering algorithms have been proposed. But they all face a problem of controlling...
The time series remote sensing data and meteorological satellite data offer new opportunities for understanding the earth system. Spatio-temporal data clustering becomes a kind of idea tool to explore huge data space of spatio-temporal data. Because there are many uncertainties in the huge spatio-temporal data, including fuzziness and randomness, the spatio-temporal clustering methods with uncertainties...
Spatial data clustering is an effective method to find interesting spatio-temporal clustering patterns. There are many uncertainties in sea surface temperature (SST) clustering, so clustering methods with uncertainty must be used. Type-2 fuzzy theory takes into account the uncertainty of membership grade while fuzzy C means (FCM) not. Based on the analysis of interval type-2 fuzzy C means (IT2FCM),...
Clustering Concept Hierarchies is the most important and basic procedure among the tasks of Ontology Construction to organize the knowledge. This paper proposes a method for concept hierarchies induction based on FCA from a list of concepts. In our method, the feature space is specified to be the attributes of the concept. Thus the feature extraction can be turned into the extraction of attribute...
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