Titlebook: Advances in Knowledge Discovery and Data Mining; 15th Pacific-Asia Co Joshua Zhexue Huang,Longbing Cao,Jaideep Srivastav Conference proceed

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https://doi.org/10.1007/978-3-319-67116-1ly pairwise, comparing the similarity of two examples only using their feature vectors. By building a neighborhood graph (kNN graph) using the training examples, we propose to utilize the similarity of linking structures of two nodes as an additional similarity measure. The structural similarity mea

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Disorders of Neuromuscular Transmission,giving the completed data to the learning algorithm. However, this is especially problematic when the features are nominal. This work presents “classifier-based nominal imputation” (CNI), an easy-to-implement and effective nominal imputation technique that views nominal imputation as classification:

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Brittany M. Rudy PhD,Sophia Zavrou PsyDnto matrix factors by optimizing a least square objective function using iterative updating algorithms, . HOSVD (High Order Singular Value Decomposition) and ParaFac (Parallel Factors). One fundamental problem of these algorithms remains unsolved: are the solutions found by these algorithms global o

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Amanda L. Thompson,Megan Connollyatial correlation among non-spatial attributes across space. However, most existing spatial clustering methods ignore spatial correlation, considering spatial and non-spatial attributes independently. In this paper, we first prove that spatial entropy is a monotonic decreasing function for non-spati

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