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This paper develops three weighted Gaussian process regression (GPR) approaches for multivariate modelling. Taking into account weighted strategy in the traditional univariate GPR, the heteroscedastic noise problem has been solved. The present paper extends the univariate weighted GPR algorithm to the multivariate case. Considering the correlation and weight between data, as well as the correlation...
An outlier is the object which is very different from the rest of the dataset on some measure. Finding such exception has received much attention in the data mining field. In this paper, we propose a KNN based outlier detection algorithm which is consisted of two phases. Firstly, it partitions the dataset into several clusters and then in each cluster, it calculates the Kth nearest neighborhood for...
Recently, spectral clustering has wide application in pattern recognition and data mining because it can obtain global optima solution and adapt to sample spaces with any shape. Thus, a spectral clustering algorithm based on normalized cuts is proposed in this paper. It selects the k eigenvalues and corresponding eigenvectors of a given stochastic matrix and clusters in n times k sub-space. Experimental...
Since an outlier often contains useful information, outlier detection is becoming a hot issue in data mining. Thus, an efficient outlier mining algorithm based on KNN is proposed in this paper. It can find outlier more accurately through defining a correlation matrix considering the importance and correlation between attributes. In addition, a data structure R-tree is used in the algorithm and it...
Outlier detection is widely used for many areas such as credit card fraud detection, discovery of criminal activities in electronic commerce, weather prediction and marketing. In this paper, we demonstrate the effectiveness of spectral clustering in dataset with outliers. Through spectral method we can use the information of feature space with eigenvectors rather than that of the whole dataset to...
Spectral clustering has become one of the most popular modern clustering algorithms because it has "global" optimal solution compared with traditional clustering methods. In this paper, we propose a modified NJW algorithm which is based on matrix perturbation theory and can be easily implemented. In addition, the algorithm can estimate the parameter k and in turn achieve appropriate clusters...
Recently, spectral clustering has become one of the most popular modern clustering algorithms which are mainly applied to image segmentation. In this paper, we propose a new spectral clustering algorithm and attempt to use it for outlier detection in dataset. Our algorithm takes the number of neighborhoods shared by the objects as the similarity measure to construct a spectral graph. It can help to...
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