Surface reconstruction method for measurement data with outlier detection by using improved RANSAC and correction parameter

Outlier Detection by Consistent Data Selection Method

Often the challenge associated with tasks like fraud and spam detection[1] is the lack of all likely patterns needed to train suitable supervised learning models. In order to overcome this limitation, such tasks are attempted as outlier or anomaly detection tasks. We also hypothesize that outliers have behavioral patterns that change over time. Limited data and continuously changing patterns ma...

Outlier Detection using Improved Genetic K-means

The outlier detection problem in some cases is similar to the classification problem. For example, the main concern of clustering-based outlier detection algorithms is to find clusters and outliers, which are often regarded as noise that should be removed in order to make more reliable clustering. In this article, we present an algorithm that provides outlier detection and data clustering simul...

Outlier Detection with Uncertain Data

In recent years, many new techniques have been developed for mining and managing uncertain data. This is because of the new ways of collecting data which has resulted in enormous amounts of inconsistent or missing data. Such data is often remodeled in the form of uncertain data. In this paper, we will examine the problem of outlier detection with uncertain data sets. The outlier detection probl...

Precise Gene Expression Measurement with Outlier Detection

Improved Centroid Peak Detection and Mass Accuracy using a Novel, Fast Data Reconstruction Method

All data are corrupted by noise and the error in centroids using algebraic algorithms is related to abnormalities in the noise over the peaks. Algebraic calculations are very fast and they always work directly on the data. However, they do not take into account a varying noise level (leading to both over and under estimation of noise levels within the data set) and one cannot assess the error o...