# Graphs from Features: Tree-Based Graph Layout for Feature Analysis
## 5 Results
### Graphs from Features
#### 5.1.2 Identifying Groups of features  
In the process of trying to find a useful set of features to describe a phenomenon, categorical data can guide understanding of what the data can tell. There are various analysis scenarios in multidimensional visualization. In many cases there is a target variable or feature, such as a class or an event that one wishes to predict. Frequently, in exploratory situations, there is more than one phenomenon represented in the data, which are described by different sets of features. For instance, in medical records, while the initial intention could be the detection of risk factors for a particular disease, it is possible that the data collection can lead to understanding other underlying conditions that are frequent in patients. Another scenario is that of collecting too much information because of the possibilities yielded by sensors, simulations and algorithms. This leads to redundancy, contradiction, and varied degrees of relevance among variables. In all of these scenarios one wishes to identify groups of features that are similar, either to simplify the representation discarding highly similar features or to identify different events in the same dataset. Graph representations of features such as the one employed here can lend themselves to locating such groups of features and using that information to shape the data analysis. We exemplify this argument by the case of a soundscape ecology, or soundscape for short, which are recordings taken in a certain landscape (most frequently partly or totally preserved) in order to understand that environment and its changes via audio. The applications vary largely, from trying  
Figure 3: News data. (**a**) A graph from features (left) connecting the top 3% similarities between 612 features. A t-SNE projection (right) was generated with 101 features selected from the graph. (**b**) A circular histogram showing the features selected from the graph (left) and an LSP projection from the same set of features (right). to distinguish different habitats and diversities to locating warning signs of environmental changes, such as the decrease in the population of a particular species. From a recording, many features can be extracted using the original sound wave, an spectrogram or individual sound properties specific to the environment. For each different application, a distinct subset of those features may be ideal for representation. The analysis of a soundscape dataset is shown in Figure 4. That figure illustrates various steps of employing the feature graph and its supporting visualizations in the analysis of recordings collected from two distinct forest landscapes in Costa Rica. These landscapes, although different, are contiguous in territory, which makes them share at least part of the same sounds. In Figure 4a, the minimum spanning tree for the feature graph is presented. Nodes with darker colors are features with highest relevance, calculated by the Pearson correlation against the target variable, which is the area (CostaRica1 and CostaRica2). The tree can be interpreted as having four or five different groups of branches. By adding more edges in progressive order of similarity to the graph visualization (Figure 4b) one already observes that three groups of nodes remain loosely connected. These can be interpreted as three groups of features that are highly similar among them. If the task is identifying different phenomena, one could start the analysis from the partition offered by this graph. When even more edges are added (Figure 4c) the three groups can still be identified although two of them are more connected now than in previous edge sets. Naturally all vertices will eventually be adjacent, since the underlying structure is a complete graph. However, the connection based on degree of similarity offers insight on the feature groups. Figure 4d,e are force-based drawings of the graph, showing that evolution.