On EM Estimation for Mixture of Multivariate t-Distributions
Neural Processing Letters
A Hybrid Higher Order Neural Classifier for handling classification problems
Expert Systems with Applications: An International Journal
Data mining using an adaptive HONN model with hyperbolic tangent neurons
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Mining incomplete data: a rough set approach
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
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Based on independent component analysis (ICA) and self-organizing maps (SOM), this paper proposes an ISOM-DH model for the incomplete data’s handling in data mining. Under these circumstances the data remain dependent and non-Gaussian, this model can make full use of the information of the given data to estimate the missing data and can visualize the handled high-dimensional data. Compared with mixture of principal component analyzers (MPCA), mean method and standard SOM-based fuzzy map model, ISOM-DH model can be applied to more cases, thus performing its superiority. Meanwhile, the correctness and reasonableness of ISOM-DH model is also validated by the experiment carried out in this paper.