TREENSIGHT
Tree insight: a path toward Data Science for all
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Benefits
- Giving users back control of the data analysis process
- Shorten and refocus the decision-making chain
- Interactively visualize and explore up to one million observations in real time
Key words
- Data analysis
- Graphical interface
- Clustering and decision Trees
- Human Computer Interface
Intellectual Property
- 1 software
Laboratory
- LIG
Institutions
- CNRS
- GRENOBLE INP-UGA
- INRIA
- UGA
Linksium Continuum
- Maturation
- Incubation
Context
Treensight is a generic and extensible graphical user interface that allows interactive visualization and manipulation of clustering and decision trees with keeping a tight link with the data. Treensight ensures an efficient collaboration user/machine by offering a better control on the analyzed data and a detailed insight into each cluster.
Technology
Treensight is a multiplatform application (macOS, Linux, Windows). It has three components:
- The Core (Javascript): it proposes visualization, filtering and exploration technique up to 1M data.
- An extensible library of scripts (Python): implementation of Machine Learning, analysis and manipulation functions.
- Communication protocole: ensures the link between the Core and the library of scripts.
Advantages
Treensight has four main features:
- Dynamic content: information about the selected cluster is displayed in the right-hand panel: name, statistics about the instances, distances, etc.
- Instances repartition: the left-hand panel allows the creation of « View points ». In each one the user selects coloring rules and visualization modes that will be displayed in the central panel.
- Variables comparison: user can compare the statistics of a variable coming from different clusters.
- Clusters comparison: in the same fashion, user can compare the clusters and their evolution.
State of progress
A first cross-platform (Windows, Linus, MacOS) version of the software is released, with restricted access. This software is used in several projects by researchers from various disciplines.
Applications
- Science and technology: classification, research of indicators, maintenance...
- Medical and health field: cohort analysis, sensitivity/specificity study...
- Marketing and economy: impact of a campaign, user profiling...
- Sociology: study of populations and their evolution, analysis of surveys...
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