Doubtful real estate transactions, with the prices far away from the market prices, appear because of non commercial transactions or efforts in order to hide the taxes. To estimate the right values of parameters, such data must be removed from a data set or robust methods of parameters estimation are to be used, while developing a mass appraisal model. Such transactions are outlying observation...

Breast cancer is one of the most widespread and deadly cancer for women. Early diagnosis and treatment of breast cancer can enhance the outcome of the patients. Due to the difficulties of outlier and skewed data, the prediction of breast cancer survey has presented many challenges in the field of data mining and pattern recognition .To solve these troubles, we have proposed an automated breast ...

The purpose of this article is to evaluate the application of forecasting models along with the use of residual control charts to assess production processes whose samples have autocorrelation characteristics. The main objective is to determine the efficiency of control charts for individual observations (CCIO) and exponentially weighted moving average (EWMA) charts when they are applied to res...

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 ...

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