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Higher-order tensor decomposition is a basis for many important data mining tasks and the efficient large-scale tensor decomposition algorithms will have positive impact on clustering, trend detection, and anomaly detection. In the paper, we develop a scalable and distributed version of the Tucker tensor decomposition, MR-T, using the Hadoop MapReduce framework. We avoid large matrix-matrix multiplication...
Spatial association mining has been used for discovering frequent spatial association patterns from large static spatial databases. When a large spatial database is updated, it is computationally expensive to redo the pattern discovery process for the updated database. This work presents the problem of finding spatial association patterns incrementally from evolving databases which are constantly...
Spatial association rule mining is a useful tool for discovering correlations and interesting relationships among spatial objects. Co-locations, or sets of spatial events which are frequently observed together in close proximity, are particularly useful for discovering their spatial dependencies. Although a number of spatial co-location mining algorithms have been developed, the computation of co-location...
Spatial association rule mining is a useful tool for discovering interesting relationships among spatial objects. Co-locations, or sets of spatial events which are frequently observed together in close proximity, are particularly useful for discovering their spatial dependencies. The computation of co-location mining is prohibitively expensive with increase in data size and spatial neighborhood. In...
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