In this paper, we show that the way internal estimates are used to measure variable
importance in Random Forests are also applicable to feature selection in unsupervised …

Unsupervised machine learning approaches involving several clustering algorithms working
together to tackle difficult data sets are a recent area of research with a large number of …

This paper introduces a new topological clustering approach to cluster high dimensional
datasets based on t-SNE (Stochastic Neighbor Embedding) dimensionality reduction …

Text processing in Information Retrieval (IR) requires text documents as primary data
sources. However, not all words in the text document are used. Some words often appear in …

Feature selection is the process of selecting a subset of optimal features by removing
redundant and irrelevant features. In supervised learning, feature selection process uses …

In this paper, we propose another extension of the Random Forests paradigm to unlabeled
data, leading to localized unsupervised feature selection (FS). We show that the way internal …

Clustering is a compression task which consists in grouping similar objects into clusters. In
real-life applications, the system may have access to several views of the same data and …

Collaborative Clustering is a data mining task the aim of which is to use several clustering
algorithms to analyze different aspects of the same data. The aim of collaborative clustering …

Automated cell counting is a required task which helps examiners in evaluating blood
smears. A problem is that clumped cells usually appear in images with various degree of …

To uncover an appropriate latent subspace for data representation, we propose in this paper
a new extension of the random forests method which leads to the unsupervised feature …