Recently many researchers are working on cluster analysis as a main tool for exploratory
data analysis and data mining. A notable feature is that specialists in different fields of …

Basic ideas and formal concepts from fuzzy sets and fuzzy logic have been used
successfully in various branches of science and engineering. This paper elaborates on the …

This paper proposes a new approach to robust clustering, in which a robust k-means
partition is derived by using a noise-rejection mechanism based on the noise-clustering …

In this paper, we propose two methods for partitioning an incomplete data set with missing
values into several linear fuzzy clusters by extracting local principal components. One is an …

In the last few decades, there has been an increase in the interest of the scientific community
for multivariate statistical techniques of data analysis in which the data are affected by …

Vertices Principal Component Analysis (V-PCA), and Centers Principal Component Analysis
(C-PCA) generalize Principal Component Analysis (PCA) in order to summarize interval …

In this paper, we analyze Xu and Yuille's robust principal component analysis (RPCA)
learning algorithms by means of the distance measurement in space. Based on the analysis …

Automated collaborative filtering is a popular technique for reducing information overload
and the task is to predict missing values in a data matrix. Extraction of local linear models is …

This paper proposes a technique to deal with fuzziness in subjective evaluation data, and
applies it to principal component analysis and correspondence analysis. In the existing …

In many situations the available amount of data is huge and can be intractable. When the
data set is single valued, latent component models are recognized techniques, which …