Outlier detection is a primary step in many data-mining applications. We present several methods for outlier detection, while distinguishing between univariate vs. multivariate techniques and parametric vs. nonparametric procedures. In presence of outliers, special attention should be taken to assure the robustness of the used estimators. Outlier detection for data mining is often based on dist...

Outliers as well as outlier patches, which widely emerge in dynamic process sampling data series, have strong bad influence on signal processing. In this paper, a series of recursive outlier-tolerant fitting algorithms are built to fit reliably the trajectories of a non-stationary sampling process when there are some outliers arising from output components of the process. Based on the recursive...

In this paper, an outlier detection method based on radial basis functions–principal component analysis (RBF-PCA) approach and Prescott method, a statistical detection approach, is proposed to detect the outlier in the complex system without clear mechanisms. Making full use of the capacity of neural networks on nonlinear mapping and the effect of Prescott method on outlier detection in linear ...

Four outlier detection methods are compared using both publicly available smaller statistical datasets and real-life Knowledge Discovery in Databases (KDD) datasets [1]. The smaller datasets provide insight (via visualisations) into the relative strengths and weaknesses of the compared methods. The real-life large datasets test scalability and practicality of application. We are unaware of prev...

This study describes an outlier detection technique for graph structure data that uses the centrality index. Existing techniques set thresholds for link and node regularity. However, existing techniques are not objective and do not apply to data without the link strength information. Therefore, we pay attention to centrality, which is an index used in network analysis. We perform outlier detect...

In this paper, we propose a new algorithm of removing outlier clusters. It is a voxel-based surface propagation method and can handle non-isolated and sharp featured surface outlier clusters in a fast way. Numerical experiments indicate the effectiveness of the algorithm in terms of accuracy and time efficiency. Key word: outlier removal, surface propagation

Is particularly useful for high dimensional data where outliers cannot be found.High dimensional data in Euclidean space pose special challenges to data. In about just the last few years, the task of unsupervised outlier detection has found.Outlier detection is an outstanding data mining task referred to open pdf with mac word class="text" href="https://tokiqivy.files.wordpress.com/2015/06/opel...

Efficient outlier detection in a large-sized big data environment incurs much of complexity in processing the information and to handle it in a proficient way. For segregating outliers from those normal data items, many of the prevailing methodologies experiences complexity in accordance with the features involved in every single attribute. On recognizing appropriate features associated the cha...