IT-map: an Effective Nonlinear Dimensionality Reduction Method for Interactive Clustering
This provides a novel solution for scientists and engineers needing to visualize and interact with clustered data, though it appears incremental as it builds on existing methods to address a specific bottleneck.
The paper tackles the crowding problem in dimensionality reduction, where clusters overlap in embeddings, by introducing IT-map, which allows local overlap but keeps clusters distinguishable through key parts, enabling interactive clustering.
Scientists in many fields have the common and basic need of dimensionality reduction: visualizing the underlying structure of the massive multivariate data in a low-dimensional space. However, many dimensionality reduction methods confront the so-called "crowding problem" that clusters tend to overlap with each other in the embedding. Previously, researchers expect to avoid that problem and seek to make clusters maximally separated in the embedding. However, the proposed in-tree (IT) based method, called IT-map, allows clusters in the embedding to be locally overlapped, while seeking to make them distinguishable by some small yet key parts. IT-map provides a simple, effective and novel solution to cluster-preserving mapping, which makes it possible to cluster the original data points interactively and thus should be of general meaning in science and engineering.