On Model-Based Clustering, Classification, and Discriminant Analysis

Model-Based Clustering, Discriminant Analysis, and Density Estimation

Cluster analysis is the automated search for groups of related observations in a dataset. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures, and most clustering methods available in commercial software are also of this type. However, there is little systematic guidance associated with these methods for solving important practical questions that...

Probabilistic model-based discriminant analysis and clustering methods in chemometrics

mortality forecasting based on lee-carter model

over the past decades a number of approaches have been applied for forecasting mortality. in 1992, a new method for long-run forecast of the level and age pattern of mortality was published by lee and carter. this method was welcomed by many authors so it was extended through a wider class of generalized, parametric and nonlinear model. this model represents one of the most influential recent d...

Text Sentiment Classification Based on Mixed Cloud Vector Model Clustering and Kernel Fisher Discriminant

In today’s world, the web has dramatically changed the way that people express their opinions. People use the internet to express their opinion, attitude, feeling and emotion about films, goods, news etc. It is challenging to automatically classify mass subjectivity comments into different sentiment orientation categories (e.g. positive/negative). Furthermore, the ambiguity and randomness, whic...

Gear Fault Diagnosis and Classification Based on Fisher Discriminant Analysis

Gears are the most essential parts in rotating machinery. So gear fault modes diagnosis and levels classification are very important in engineering practice. This paper present a novel method in gear fault recognition and identification using Fisher discriminant analysis (FDA) due to FDA can reduct dimension when analyse signal. The real data collected from a gearbox test rig is used to validat...

Process monitoring based on classification tree and discriminant analysis

To cope with the computational intensity associated with classification tree analysis and the multicolinearity in the process data, a newly developed process monitoring scheme integrating classification tree and Fisher Discriminant Analysis (FDA) is developed. FDA extracts the most significant components in the original process data and achieves optimal discriminating among different faults. Cl...