Efficient robust methods via monitoring for clustering and multivariate data analysis

Robust methods for multivariate data analysis

*Correspo Danish In Denmark, E-mail: sf Contract/ Fisheries. Outliers may hamper proper classical multivariate analysis, and lead to incorrect conclusions. To remedy the problem of outliers, robust methods are developed in statistics and chemometrics. Robust methods reduce or remove the effect of outlying data points and allow the ‘good’ data to primarily determine the result. This article revi...

Generalized Clustering Methods for Multivariate Data

Efficient analysis (including segmentation and classification) of multivariate data is an inherently complex task in which features occur as salient members of clusters in a multi-dimensional data space. The clusters assume a variety of distributions and frequently overlap, leading to difficulty in segmentation and classification. In many cases, similar but distinct features in the dataset are ...

Robust Monitoring of Contaminated Multivariate Data

Monitoring a process that suffers from data contamination using a traditional multivariate T 2 chart can lead to an excessive number of false alarms. This paper extends the diagnostic statistic technique of Davis and Adams [1] to the multivariate case. A traditional T 2 control chart augmented by a diagnostic statistic improves the work stoppage rates for contaminated multivariate data and main...

Root-clustering for multivariate polynomials and robust stability analysis

Monitoring Multivariate Data via KNN Learning

Process monitoring of multivariate quality attributes is important in many industrial applications, in which rich historical data are often available thanks to modern sensing technologies. While multivariate statistical process control (SPC) has been receiving increasing attention, existing methods are often inadequate as they either cannot deliver satisfactory detection performance or cannot c...