and an N*N distance (or similarity) matrix, the basic process of hierarchical clustering (defined by S.C. Johnson in 1967) is this: Start by assigning each item to a cluster, so that if you have N items, you now have N clusters, each containing just one item. Let the distances (si...
Numerous decision problems related, for example, to environmental monitoring, regional solid waste management, manufacturing systems, transportation services, and so forth, depend essentially on the choice of a relatively small number of 'primary facilities' (such as, for example, water quality analysis...
Another example is in the analysis of bibliographic data, where different types of link exist among authors, conferences, journals and papers. These considerations motivate the recent interest in mining heterogeneous information networks, which in most cases focus on the clustering task. Typically, (...
To compare diseases, we were interested in strong biologically meaningful comparisons, for example between current smokers and a baseline of never smokers, as opposed to a baseline of previous smokers. Such substantial differences are more likely to be associated with changes to biological pathways tha...
Classification and prediction of toxicity of chemicals using an automated phenotypic profiling of Caenorhabditis elegansC. elegans, Chemicals, Toxicity, Image ... S Gao,W Chen,Y Zeng,... - 《Bmc Pharmacol Toxicol》 被引量: 0发表: 2018年 Quantitative Classification and Natural Clustering of Caenorh...
They can perform a wide number of tasks ranging from preprocessing, classification, regression, and clustering to association rules [8]. 17.2.2 Requirements of clustering 1. High dimensionality: A clustering algorithm should be able to manage low-dimensional data and high-dimensional space ...
A Python toolkit/library for reality-centric machine/deep learning and data mining on partially-observed time series, including SOTA neural network models for scientific analysis tasks of imputation/classification/clustering/forecasting/anomaly detection/cleaning on incomplete industrial (irregularly-sampled) ...
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Nevertheless, the performances of classification and clustering methods are considerably caused by the increasing dataset dimension because the algorithm in this category operates on the dataset dimension. Additionally, the drawback of higher dimension datasets includes redundant data, higher module construct...
[18], and GMCC [30]), GMEE can also be applied to KF family algorithms (using (8) to replace the (23) in the literature [28], and some derivations similar to literature [28] can be developed to obtain a new KF algorithm), clustering [31], feature selection [32], and other ...