Gilles Bisson

Event-based information extraction for the biomedical domain: the Caderige project

This paper gives an overview of the Caderige project. This project involves teams from different areas (biology, machine learning, natural language processing) in order to develop highlevel analysis tools for extracting structured information from biological bibliographical databases, especially Medline. The paper gives an overview of the approach and compares it to the state of the art.

Improving Visualization of Large Hierarchical Clustering

The classical representation of a binary tree generated by a hierarchical clustering is a node-link-based visualization denoted as a dendrogram. It allows users to explore in a simple way the clusters and the relationships between instances. However, exploration of large dendrograms is known to be difficult due to the graphical and cognitive information overload involved. Here, we discuss the current approaches and we introduce Stacked Trees, a new Focus+Context visualization technique that allows the exploration of the hierarchical clustering of up to fifty thousands nodes on a standard-sized screen.

Dendrogramix: A hybrid tree-matrix visualization technique to support interactive exploration of dendrograms

Clustering is often a first step when trying to make sense of a large data set. A wide family of cluster analysis algorithms, namely hierarchical clustering algorithms, does not provide a partition of the data set but a hierarchy of clusters organized in a binary tree, known as a dendrogram. The dendrogram has a classical node-link representation used by experts for various tasks like: to decide which subtrees are actual clusters (e.g., by cutting the dendrogram at a given depth); to give those clusters a name by inspecting their content; etc. We present Dendrogramix, a hybrid tree-matrix interactive visualization of dendrograms that superimposes the relationship between individual objects on to the hierarchy of clusters. Dendrogramix enables users to do tasks which involve both clusters and individual objects that are impracticable with the classical representation, like: to explain why a particular objects belongs to a particular cluster; to elicit and understand uncommon patterns (e.g., objects that could have been classified in a totally different cluster); etc. Those sensemaking tasks are supported by a consistent set of interaction techniques that facilitates the exploration of large clustering results.

Guiding the search in the NO region of the phase transition problem with a partial subsumption test

We introduce a test, named π-subsumption, which computes partial subsumptions between a hypothesis h and an example e, as well as a measure, the subsumption index, which quantifies the covering degree between h and e. The behavior of this measure is studied on the phase transition problem.

Clustering of Molecules: Influence of the Similarity Measures

In this paper, we present the results of an experimental study to analyze the effect of various similarity (or distance) measures on the clustering quality of a set of molecules. We mainly focused on the clustering approaches able to directly deal with the 2D representation of the molecules (i.e., graphs). In such a context, we found that it seems relevant to use an approach based on asymmetrical measures of similarity. Our experiments are carried out on a dataset coming from the High Throughput Screening HTS domain.

Multi-operator Decision Trees for Explainable Time-Series Classification

Analyzing time-series is a task of rising interest in machine learning. At the same time developing interpretable machine learning tools is the recent challenge proposed by the industry to ease use of these tools by engineers and domain experts. In the paper we address the problem of generating interpretable classification of time-series data. We propose to extend the classical decision tree machine learning algorithm to Multi-operator Temporal Decision Trees (MTDT). The resulting algorithm provides interpretable decisions, thus improving the results readability, while preserving the classification accuracy. Aside MTDT we provide an interactive visualization tool allowing a user to analyse the data, their intrinsic regularities and the learned tree model.

Clustering Libraries of Compounds into Families: Asymmetry-Based Similarity Measure to Categorize Small Molecules

Clustering Libraries of Compounds into Families: Asymmetry-Based Similarity Measure to Categorize Small Molecules