Outliers Detection in Statistical Quality Control Using Support Vector Data Description

Outliers have been informally defined as observations in a data set which appear to be inconsistent with the remainder of that set of data, or which deviate so much from other observations so as to arouse suspicions that they were generated by a different mechanism [1]. The identification of outliers can lead to the discovery of useful knowledge and has a number of practical applications. The boundary of a dataset can be used to detect novel data or outliers. Outlier detection belongs to the most important tasks in data analysis. The outliers describe the abnormal data behavior, i.e. data that are deviating from the natural data variability Outlier detection has many applications, such as data cleaning and fraud detection. Frequently, outliers are removed to improve accuracy of the estimators. But sometimes the presence of an outlier has a certain meaning which explanation can be lost if the outlier is deleted. Often outliers are of primary interest, for example in geochemical exploration they are indications for mineral deposits. The cut-off value or threshold, which divides anomalous and non-anomalous data numerically, is often the basis for important decisions. It is tried here to find the best representation of a dataset such that the target class may best be distinguish from the real data. In this paper outlier in a dataset of percentage of different indigrends for copper production is studied using support vector data analysis for its quality study.