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Analyzing notor imagery electrocardiogram (ECoG) signal is very challenging for it is hard to set up a classifier based on the labeled ECoG obtained in the first session and apply it to the unlabeled test data obtained in the second session. Here we propose a new approach to analyze ECoG trails in the case of session-to-session transfer exists. In our approach, firstly, dimension reduction is performed...
Software metrics are collected at various phases of the software development process. These metrics contain the information of the software and can be used to predict software quality in the early stage of software life cycle. Intelligent computing techniques such as data mining can be applied in the study of software quality by analyzing software metrics. Clustering analysis, which can be considered...
DNA microarray technique has produced large amount of gene expression data. To analyze these data, many excellent machine learning techniques have been proposed in recent related work. In this paper, we try to perform the clustering of microarray data by combining the recently proposed locality preserving projection (LPP) method with PCA, i.e. PCA-LPP. The comparison between PCA and PCA-LPP is performed...
Mean-shift analysis is a general nonparametric clustering technique based on density estimation for the analysis of complex feature spaces. It has been successfully applied to many applications such as segmentation and tracking. However, despite its promising performance, there are applications for which the algorithm converges too slowly and is not practical. In this paper, an improved version of...
In this paper, we proposed an effective method to classify traffic vehicle based on fuzzy theory. Such method uses two-hierarchy synthesis evaluation model based on fuzzy clustering which can accurately reflect practical situations. It can not only reduce influences caused by subjective factors greatly, but also be performed in a highly efficient way, satisfied real-time requisition. The results of...
Outlier detection is one of the branches of data mining, with important applications in the domains of finance fraud detection, network intrusion analysis and so on. But most applications are high dimensional domains. Many algorithms use the concept of proximity to find outliers based on the relationship to the data set. However, the sparsity of high dimensional points results to the algorithms are...
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