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The study of clustering and classification of uncertain data addresses the challenges posed by imprecise, noisy, or inherently probabilistic measurements common in many modern data acquisition ...
Data mining techniques have been widely used for extracting knowledge from large amounts of data. Monitoring deforestation is utmost important for the developing countries.
Incomplete data affects classification accuracy and hinders effective data mining. The following techniques are effective for working with incomplete data. The ISOM-DH model handles incomplete ...
Clustering is a commonly considered data mining problem in the text domains. The problem finds numerous applications in customer segmentation, classification, collaborative filtering ...
Clustering and classification represent the opportunity to apply categorical reasoning to vast data contexts we would otherwise find overwhelming.
(1) The application of hierarchical classification to ecological community data is examined, using a variety of classification techniques and test data sets. Problems discussed include: (a) the choice ...
When clustering real data, both c-means and SOM classified observations into clusters that were closer together (relative to k-means) and hence had less distinct boundaries separating the clusters.